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Research Summary – Structure, Examples and Writing Guide

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Research Summary

Definition:

A research summary is a brief and concise overview of a research project or study that highlights its key findings, main points, and conclusions. It typically includes a description of the research problem, the research methods used, the results obtained, and the implications or significance of the findings. It is often used as a tool to quickly communicate the main findings of a study to other researchers, stakeholders, or decision-makers.

Structure of Research Summary

The Structure of a Research Summary typically include:

Introduction : This section provides a brief background of the research problem or question, explains the purpose of the study, and outlines the research objectives.
Methodology : This section explains the research design, methods, and procedures used to conduct the study. It describes the sample size, data collection methods, and data analysis techniques.
Results : This section presents the main findings of the study, including statistical analysis if applicable. It may include tables, charts, or graphs to visually represent the data.
Discussion : This section interprets the results and explains their implications. It discusses the significance of the findings, compares them to previous research, and identifies any limitations or future directions for research.
Conclusion : This section summarizes the main points of the research and provides a conclusion based on the findings. It may also suggest implications for future research or practical applications of the results.
References : This section lists the sources cited in the research summary, following the appropriate citation style.

How to Write Research Summary

Here are the steps you can follow to write a research summary:

Read the research article or study thoroughly: To write a summary, you must understand the research article or study you are summarizing. Therefore, read the article or study carefully to understand its purpose, research design, methodology, results, and conclusions.
Identify the main points : Once you have read the research article or study, identify the main points, key findings, and research question. You can highlight or take notes of the essential points and findings to use as a reference when writing your summary.
Write the introduction: Start your summary by introducing the research problem, research question, and purpose of the study. Briefly explain why the research is important and its significance.
Summarize the methodology : In this section, summarize the research design, methods, and procedures used to conduct the study. Explain the sample size, data collection methods, and data analysis techniques.
Present the results: Summarize the main findings of the study. Use tables, charts, or graphs to visually represent the data if necessary.
Interpret the results: In this section, interpret the results and explain their implications. Discuss the significance of the findings, compare them to previous research, and identify any limitations or future directions for research.
Conclude the summary : Summarize the main points of the research and provide a conclusion based on the findings. Suggest implications for future research or practical applications of the results.
Revise and edit : Once you have written the summary, revise and edit it to ensure that it is clear, concise, and free of errors. Make sure that your summary accurately represents the research article or study.
Add references: Include a list of references cited in the research summary, following the appropriate citation style.

Example of Research Summary

Here is an example of a research summary:

Title: The Effects of Yoga on Mental Health: A Meta-Analysis

Introduction: This meta-analysis examines the effects of yoga on mental health. The study aimed to investigate whether yoga practice can improve mental health outcomes such as anxiety, depression, stress, and quality of life.

Methodology : The study analyzed data from 14 randomized controlled trials that investigated the effects of yoga on mental health outcomes. The sample included a total of 862 participants. The yoga interventions varied in length and frequency, ranging from four to twelve weeks, with sessions lasting from 45 to 90 minutes.

Results : The meta-analysis found that yoga practice significantly improved mental health outcomes. Participants who practiced yoga showed a significant reduction in anxiety and depression symptoms, as well as stress levels. Quality of life also improved in those who practiced yoga.

Discussion : The findings of this study suggest that yoga can be an effective intervention for improving mental health outcomes. The study supports the growing body of evidence that suggests that yoga can have a positive impact on mental health. Limitations of the study include the variability of the yoga interventions, which may affect the generalizability of the findings.

Conclusion : Overall, the findings of this meta-analysis support the use of yoga as an effective intervention for improving mental health outcomes. Further research is needed to determine the optimal length and frequency of yoga interventions for different populations.

References :

Cramer, H., Lauche, R., Langhorst, J., Dobos, G., & Berger, B. (2013). Yoga for depression: a systematic review and meta-analysis. Depression and anxiety, 30(11), 1068-1083.
Khalsa, S. B. (2004). Yoga as a therapeutic intervention: a bibliometric analysis of published research studies. Indian journal of physiology and pharmacology, 48(3), 269-285.
Ross, A., & Thomas, S. (2010). The health benefits of yoga and exercise: a review of comparison studies. The Journal of Alternative and Complementary Medicine, 16(1), 3-12.

Purpose of Research Summary

The purpose of a research summary is to provide a brief overview of a research project or study, including its main points, findings, and conclusions. The summary allows readers to quickly understand the essential aspects of the research without having to read the entire article or study.

Research summaries serve several purposes, including:

Facilitating comprehension: A research summary allows readers to quickly understand the main points and findings of a research project or study without having to read the entire article or study. This makes it easier for readers to comprehend the research and its significance.
Communicating research findings: Research summaries are often used to communicate research findings to a wider audience, such as policymakers, practitioners, or the general public. The summary presents the essential aspects of the research in a clear and concise manner, making it easier for non-experts to understand.
Supporting decision-making: Research summaries can be used to support decision-making processes by providing a summary of the research evidence on a particular topic. This information can be used by policymakers or practitioners to make informed decisions about interventions, programs, or policies.
Saving time: Research summaries save time for researchers, practitioners, policymakers, and other stakeholders who need to review multiple research studies. Rather than having to read the entire article or study, they can quickly review the summary to determine whether the research is relevant to their needs.

Characteristics of Research Summary

The following are some of the key characteristics of a research summary:

Concise : A research summary should be brief and to the point, providing a clear and concise overview of the main points of the research.
Objective : A research summary should be written in an objective tone, presenting the research findings without bias or personal opinion.
Comprehensive : A research summary should cover all the essential aspects of the research, including the research question, methodology, results, and conclusions.
Accurate : A research summary should accurately reflect the key findings and conclusions of the research.
Clear and well-organized: A research summary should be easy to read and understand, with a clear structure and logical flow.
Relevant : A research summary should focus on the most important and relevant aspects of the research, highlighting the key findings and their implications.
Audience-specific: A research summary should be tailored to the intended audience, using language and terminology that is appropriate and accessible to the reader.
Citations : A research summary should include citations to the original research articles or studies, allowing readers to access the full text of the research if desired.

When to write Research Summary

Here are some situations when it may be appropriate to write a research summary:

Proposal stage: A research summary can be included in a research proposal to provide a brief overview of the research aims, objectives, methodology, and expected outcomes.
Conference presentation: A research summary can be prepared for a conference presentation to summarize the main findings of a study or research project.
Journal submission: Many academic journals require authors to submit a research summary along with their research article or study. The summary provides a brief overview of the study’s main points, findings, and conclusions and helps readers quickly understand the research.
Funding application: A research summary can be included in a funding application to provide a brief summary of the research aims, objectives, and expected outcomes.
Policy brief: A research summary can be prepared as a policy brief to communicate research findings to policymakers or stakeholders in a concise and accessible manner.

Advantages of Research Summary

Research summaries offer several advantages, including:

Time-saving: A research summary saves time for readers who need to understand the key findings and conclusions of a research project quickly. Rather than reading the entire research article or study, readers can quickly review the summary to determine whether the research is relevant to their needs.
Clarity and accessibility: A research summary provides a clear and accessible overview of the research project’s main points, making it easier for readers to understand the research without having to be experts in the field.
Improved comprehension: A research summary helps readers comprehend the research by providing a brief and focused overview of the key findings and conclusions, making it easier to understand the research and its significance.
Enhanced communication: Research summaries can be used to communicate research findings to a wider audience, such as policymakers, practitioners, or the general public, in a concise and accessible manner.
Facilitated decision-making: Research summaries can support decision-making processes by providing a summary of the research evidence on a particular topic. Policymakers or practitioners can use this information to make informed decisions about interventions, programs, or policies.
Increased dissemination: Research summaries can be easily shared and disseminated, allowing research findings to reach a wider audience.

Limitations of Research Summary

Limitations of the Research Summary are as follows:

Limited scope: Research summaries provide a brief overview of the research project’s main points, findings, and conclusions, which can be limiting. They may not include all the details, nuances, and complexities of the research that readers may need to fully understand the study’s implications.
Risk of oversimplification: Research summaries can be oversimplified, reducing the complexity of the research and potentially distorting the findings or conclusions.
Lack of context: Research summaries may not provide sufficient context to fully understand the research findings, such as the research background, methodology, or limitations. This may lead to misunderstandings or misinterpretations of the research.
Possible bias: Research summaries may be biased if they selectively emphasize certain findings or conclusions over others, potentially distorting the overall picture of the research.
Format limitations: Research summaries may be constrained by the format or length requirements, making it challenging to fully convey the research’s main points, findings, and conclusions.
Accessibility: Research summaries may not be accessible to all readers, particularly those with limited literacy skills, visual impairments, or language barriers.

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How To Write A Research Summary

It’s a common perception that writing a research summary is a quick and easy task. After all, how hard can jotting down 300 words be? But when you consider the weight those 300 words carry, writing a research summary as a part of your dissertation, essay or compelling draft for your paper instantly becomes daunting task.

A research summary requires you to synthesize a complex research paper into an informative, self-explanatory snapshot. It needs to portray what your article contains. Thus, writing it often comes at the end of the task list.

Regardless of when you’re planning to write, it is no less of a challenge, particularly if you’re doing it for the first time. This blog will take you through everything you need to know about research summary so that you have an easier time with it.

What is a Research Summary?

A research summary is the part of your research paper that describes its findings to the audience in a brief yet concise manner. A well-curated research summary represents you and your knowledge about the information written in the research paper.

While writing a quality research summary, you need to discover and identify the significant points in the research and condense it in a more straightforward form. A research summary is like a doorway that provides access to the structure of a research paper's sections.

Since the purpose of a summary is to give an overview of the topic, methodology, and conclusions employed in a paper, it requires an objective approach. No analysis or criticism.

Research summary or Abstract. What’s the Difference?

They’re both brief, concise, and give an overview of an aspect of the research paper. So, it’s easy to understand why many new researchers get the two confused. However, a research summary and abstract are two very different things with individual purpose. To start with, a research summary is written at the end while the abstract comes at the beginning of a research paper.

A research summary captures the essence of the paper at the end of your document. It focuses on your topic, methods, and findings. More like a TL;DR, if you will. An abstract, on the other hand, is a description of what your research paper is about. It tells your reader what your topic or hypothesis is, and sets a context around why you have embarked on your research.

Getting Started with a Research Summary

Before you start writing, you need to get insights into your research’s content, style, and organization. There are three fundamental areas of a research summary that you should focus on.

While deciding the contents of your research summary, you must include a section on its importance as a whole, the techniques, and the tools that were used to formulate the conclusion. Additionally, there needs to be a short but thorough explanation of how the findings of the research paper have a significance.
To keep the summary well-organized, try to cover the various sections of the research paper in separate paragraphs. Besides, how the idea of particular factual research came up first must be explained in a separate paragraph.
As a general practice worldwide, research summaries are restricted to 300-400 words. However, if you have chosen a lengthy research paper, try not to exceed the word limit of 10% of the entire research paper.

How to Structure Your Research Summary

The research summary is nothing but a concise form of the entire research paper. Therefore, the structure of a summary stays the same as the paper. So, include all the section titles and write a little about them. The structural elements that a research summary must consist of are:

It represents the topic of the research. Try to phrase it so that it includes the key findings or conclusion of the task.

The abstract gives a context of the research paper. Unlike the abstract at the beginning of a paper, the abstract here, should be very short since you’ll be working with a limited word count.

Introduction

This is the most crucial section of a research summary as it helps readers get familiarized with the topic. You should include the definition of your topic, the current state of the investigation, and practical relevance in this part. Additionally, you should present the problem statement, investigative measures, and any hypothesis in this section.

Methodology

This section provides details about the methodology and the methods adopted to conduct the study. You should write a brief description of the surveys, sampling, type of experiments, statistical analysis, and the rationality behind choosing those particular methods.

Create a list of evidence obtained from the various experiments with a primary analysis, conclusions, and interpretations made upon that. In the paper research paper, you will find the results section as the most detailed and lengthy part. Therefore, you must pick up the key elements and wisely decide which elements are worth including and which are worth skipping.

This is where you present the interpretation of results in the context of their application. Discussion usually covers results, inferences, and theoretical models explaining the obtained values, key strengths, and limitations. All of these are vital elements that you must include in the summary.

Most research papers merge conclusion with discussions. However, depending upon the instructions, you may have to prepare this as a separate section in your research summary. Usually, conclusion revisits the hypothesis and provides the details about the validation or denial about the arguments made in the research paper, based upon how convincing the results were obtained.

The structure of a research summary closely resembles the anatomy of a scholarly article . Additionally, you should keep your research and references limited to authentic and scholarly sources only.

Tips for Writing a Research Summary

The core concept behind undertaking a research summary is to present a simple and clear understanding of your research paper to the reader. The biggest hurdle while doing that is the number of words you have at your disposal. So, follow the steps below to write a research summary that sticks.

1. Read the parent paper thoroughly

You should go through the research paper thoroughly multiple times to ensure that you have a complete understanding of its contents. A 3-stage reading process helps.

a. Scan: In the first read, go through it to get an understanding of its basic concept and methodologies.

b. Read: For the second step, read the article attentively by going through each section, highlighting the key elements, and subsequently listing the topics that you will include in your research summary.

c. Skim: Flip through the article a few more times to study the interpretation of various experimental results, statistical analysis, and application in different contexts.

Sincerely go through different headings and subheadings as it will allow you to understand the underlying concept of each section. You can try reading the introduction and conclusion simultaneously to understand the motive of the task and how obtained results stay fit to the expected outcome.

2. Identify the key elements in different sections

While exploring different sections of an article, you can try finding answers to simple what, why, and how. Below are a few pointers to give you an idea:

What is the research question and how is it addressed?
Is there a hypothesis in the introductory part?
What type of methods are being adopted?
What is the sample size for data collection and how is it being analyzed?
What are the most vital findings?
Do the results support the hypothesis?

Discussion/Conclusion

What is the final solution to the problem statement?
What is the explanation for the obtained results?
What is the drawn inference?
What are the various limitations of the study?

3. Prepare the first draft

Now that you’ve listed the key points that the paper tries to demonstrate, you can start writing the summary following the standard structure of a research summary. Just make sure you’re not writing statements from the parent research paper verbatim.

Instead, try writing down each section in your own words. This will not only help in avoiding plagiarism but will also show your complete understanding of the subject. Alternatively, you can use a summarizing tool (AI-based summary generators) to shorten the content or summarize the content without disrupting the actual meaning of the article.

SciSpace Copilot is one such helpful feature! You can easily upload your research paper and ask Copilot to summarize it. You will get an AI-generated, condensed research summary. SciSpace Copilot also enables you to highlight text, clip math and tables, and ask any question relevant to the research paper; it will give you instant answers with deeper context of the article..

4. Include visuals

One of the best ways to summarize and consolidate a research paper is to provide visuals like graphs, charts, pie diagrams, etc.. Visuals make getting across the facts, the past trends, and the probabilistic figures around a concept much more engaging.

5. Double check for plagiarism

It can be very tempting to copy-paste a few statements or the entire paragraphs depending upon the clarity of those sections. But it’s best to stay away from the practice. Even paraphrasing should be done with utmost care and attention.

Also: QuillBot vs SciSpace: Choose the best AI-paraphrasing tool

6. Religiously follow the word count limit

You need to have strict control while writing different sections of a research summary. In many cases, it has been observed that the research summary and the parent research paper become the same length. If that happens, it can lead to discrediting of your efforts and research summary itself. Whatever the standard word limit has been imposed, you must observe that carefully.

7. Proofread your research summary multiple times

The process of writing the research summary can be exhausting and tiring. However, you shouldn’t allow this to become a reason to skip checking your academic writing several times for mistakes like misspellings, grammar, wordiness, and formatting issues. Proofread and edit until you think your research summary can stand out from the others, provided it is drafted perfectly on both technicality and comprehension parameters. You can also seek assistance from editing and proofreading services , and other free tools that help you keep these annoying grammatical errors at bay.

8. Watch while you write

Keep a keen observation of your writing style. You should use the words very precisely, and in any situation, it should not represent your personal opinions on the topic. You should write the entire research summary in utmost impersonal, precise, factually correct, and evidence-based writing.

9. Ask a friend/colleague to help

Once you are done with the final copy of your research summary, you must ask a friend or colleague to read it. You must test whether your friend or colleague could grasp everything without referring to the parent paper. This will help you in ensuring the clarity of the article.

Once you become familiar with the research paper summary concept and understand how to apply the tips discussed above in your current task, summarizing a research summary won’t be that challenging. While traversing the different stages of your academic career, you will face different scenarios where you may have to create several research summaries.

In such cases, you just need to look for answers to simple questions like “Why this study is necessary,” “what were the methods,” “who were the participants,” “what conclusions were drawn from the research,” and “how it is relevant to the wider world.” Once you find out the answers to these questions, you can easily create a good research summary following the standard structure and a precise writing style.

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Research Summary Structure, Samples, Writing Steps, and Useful Suggestions

Updated 28 Aug 2024

What is a Research Summary and Why Is It Important?

A research summary is a type of paper designed to provide a brief overview of a given study - typically, an article from a peer-reviewed academic journal. It is a frequent type of task encountered in US colleges and universities, both in humanitarian and exact sciences, which is due to how important it is to teach students to properly interact with and interpret scientific literature and in particular, academic papers, which are the key way through which new ideas, theories, and evidence are presented to experts in many fields of knowledge. A research summary typically preserves the structure/sections of the article it focuses on. Get the grades you want with our professional research paper helper .

How to Write a Research Summary – Typical Steps

Follow these clear steps to help avoid typical mistakes and productivity bottlenecks, allowing for a more efficient through your writing process:

Skim the article in order to get a rough idea of the content covered in each section and to understand the relative importance of content, for instance, how important different lines of evidence are (this helps you understand which sections you should focus on more when reading in detail). Make sure you understand the task and your professor's requirements before reading the article. In this step, you can also decide whether to write a summary by yourself or ask for a cheap research paper writing service instead.
Analyze and understand the topic and article. Writing a summary of a research paper involves becoming very familiar with the topic – sometimes, it is impossible to understand the content without learning about the current state of knowledge, as well as key definitions, concepts, models. This is often performed while reading the literature review. As for the paper itself, understanding it means understanding analysis questions, hypotheses, listed evidence, how strongly this evidence supports the hypotheses, as well as analysis implications. Keep in mind that only a deep understanding allows one to efficiently and accurately summarize the content.
Make notes as you read. You could highlight or summarize each paragraph with a brief sentence that would record the key idea delivered in it (obviously, some paragraphs deserve more attention than others). However, be careful not to engage in extensive writing while still reading. This is important because, while reading, you might realize that some sections you initially considered important might actually be less important compared to information that follows. As for underlining or highlighting – do these only with the most important evidence, otherwise, there is little use in “coloring” everything without distinction.
Assemble a draft by bringing together key evidence and notes from each paragraph/ section. Make sure that all elements characteristic of a research summary are covered (as detailed below).
Find additional literature for forming or supporting your critical view (this is if your critical view/position is required), for instance, judgments about limitations of the study or contradictory evidence.

Read Also: Criminal Justice Research Topics To Impress Your Teacher

Research Summary Structure

The research summary format resembles that found in the original paper (just a concise version of it). Content from all sections should be covered and reflected upon, regardless of whether corresponding headings are present or not. Key structural elements of any research summary are as follows:

Title – it announces the exact topic/area of analysis and can even be formulated to briefly announce key finding(s) or argument(s) delivered.
Abstract – this is a very concise and comprehensive description of the study, present virtually in any academic article (the length varies greatly, typically within 100-500 words). Unlike an academic article, your research summary is expected to have a much shorter abstract.
Introduction – this is an essential part of any research summary which provides necessary context (the literature review) that helps introduce readers to the subject by presenting the current state of the investigation, an important concept or definition, etc. This section might also describe the subject’s importance (or might not, for instance, when it is self-evident). Finally, an introduction typically lists investigation questions and hypotheses advanced by authors, which are normally mentioned in detail in any research summary (obviously, doing this is only possible after identifying these elements in the original paper).
Methodology – regardless of its location, this section details experimental methods or data analysis methods used (e.g. types of experiments, surveys, sampling, or statistical analysis). In a research summary, many of these details would have to be omitted; hence, it is important to understand what is most important to mention.
Results section – this section lists in detail evidence obtained from all experiments with some primary data analysis, conclusions, observations, and primary interpretations being made. It is typically the largest section of any analysis paper, so, it has to be concisely rewritten, which implies understanding which content is worth omitting and worth keeping.
Discussion – this is where results are being discussed in the context of current knowledge among experts. This section contains interpretations of results, theoretical models explaining the observed results, study strengths and especially limitations, complementary future exploration to be undertaken, conclusions, etc. All these are important elements that need to be conveyed in a summary.
Conclusion – in the original article, this section could be absent or merged with “Discussion”. Specific research summary instructions might require this to be a standalone section. In a conclusion, hypotheses are revisited and validated or denied, based on how convincing the evidence is (key lines of evidence could be highlighted).
References – this section is for mentioning those cited works directly in your summary – obviously, one has to provide appropriate citations at least for the original article (this often suffices). Mentioning other works might be relevant when your critical opinion is also required (supported with new unrelated evidence).

Note that if you need some model research summary papers done before you start writing yourself (this will help familiarize you with essay structure and various sections), you could simply recruit our company by following the link provided below.

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Research Summary Writing Tips

Below is a checklist of useful research paper tips worth considering when writing research summaries:

Make sure you are always aware of the bigger picture/ direction. You need to keep in mind a complete and coherent picture of the story delivered by the original article. It might be helpful to reread or scan it quickly to remind yourself of the declared goals, hypotheses, key evidence, and conclusions – this awareness offers a constant sense of direction, which ensures that no written sentence is out of context. It is useful doing this even after you have written a fourth, a third, or half of the paper (to make sure no deviation occurs).
Consider writing a detailed research outline before writing the draft – it might be of great use when structuring your paper. A research summary template is also very likely to help you structure your paper.
Sketch the main elements of the conclusion before writing it. Do this for a number of reasons: validate/invalidate hypotheses; enumerate key evidence supporting or invalidating them, list potential implications; mention the subject’s importance; mention study limitations and future directions for research. In order to include them all, it is useful having them written down and handy.
Consider writing the introduction and discussion last. It makes sense to first list hypotheses, goals, questions, and key results. Latter, information contained in the introduction and discussion can be adapted as needed (for instance, to match a preset word count limit). Also, on the basis of already written paragraphs, you can easily generate your discussion with the help of a conclusion tool ; it works online and is absolutely free of charge. Apart from this, follow a natural order.
Include visuals – you could summarize a lot of text using graphs or charts while simultaneously improving readability.
Be very careful not to plagiarize. It is very tempting to “borrow” or quote entire phrases from an article, provided how well-written these are, but you need to summarize your paper without plagiarizing at all (forget entirely about copy-paste – it is only allowed to paraphrase and even this should be done carefully). The best way to stay safe is by formulating your own thoughts from scratch.
Keep your word count in check. You don’t want your summary to be as long as the original paper (just reformulated). In addition, you might need to respect an imposed word count limit, which requires being careful about how much you write for each section.
Proofread your work for grammar, spelling, wordiness, and formatting issues (feel free to use our convert case tool for titles, headings, subheadings, etc.).
Watch your writing style – when summarizing content, it should be impersonal, precise, and purely evidence-based. A personal view/attitude should be provided only in the critical section (if required).
Ask a colleague to read your summary and test whether he/she could understand everything without reading the article – this will help ensure that you haven’t skipped some important content, explanations, concepts, etc.

For additional information on formatting, structure, and for more writing tips, check out these research paper guidelines on our website. Remember that we cover most research papers writing services you can imagine and can offer help at various stages of your writing project, including proofreading, editing, rewriting for plagiarism elimination, and style adjustment.

Research Summary Example 1

Below are some defining elements of a sample research summary written from an imaginary article.

Title – “The probability of an unexpected volcanic eruption in Yellowstone” Introduction – this section would list those catastrophic consequences hitting our country in case of a massive eruption and the importance of analyzing this matter. Hypothesis – An eruption of the Yellowstone supervolcano would be preceded by intense precursory activity manifesting a few weeks up to a few years in advance. Results – these could contain a report of statistical data from multiple volcanic eruptions happening worldwide looking specifically at activity that preceded these events (in particular, how early each type of activity was detected). Discussion and conclusion – Given that Yellowstone is continuously monitored by scientists and that signs of an eruption are normally detected much in advance and at least a few days in advance, the hypothesis is confirmed. This could find application in creating emergency plans detailing an organized evacuation campaign and other response measures.

Research Summary Example 2

Below is another sample sketch, also from an imaginary article.

Title – “The frequency of extreme weather events in US in 2000-2008 as compared to the ‘50s” Introduction – Weather events bring immense material damage and cause human victims. Hypothesis – Extreme weather events are significantly more frequent nowadays than in the ‘50s Results – these could list the frequency of several categories of extreme events now and then: droughts and associated fires, massive rainfall/snowfall and associated floods, hurricanes, tornadoes, arctic cold waves, etc. Discussion and conclusion – Several types of extreme events indeed became significantly more frequent recently, confirming this hypothesis. This increasing frequency correlates reliably with rising CO2 levels in atmosphere and growing temperatures worldwide and in the absence of another recent major global change that could explain a higher frequency of disasters but also knowing how growing temperature disturbs weather patterns, it is natural to assume that global warming (CO2) causes this increase in frequency. This, in turn, suggests that this increased frequency of disasters is not a short-term phenomenon but is here to stay until we address CO2 levels.

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Let Professionals Help With Your Research Summary

Writing a research summary has its challenges, but becoming familiar with its structure (i.e. the structure of an article), understanding well the article that needs to be summarized, and adhering to recommended guidelines will help the process go smoothly.

Simply create your account in a few clicks, place an order by uploading your instructions, and upload or indicate the article requiring a summary and choose a preferred writer for this task (according to experience, rating, bidding price). Our transparent system puts you in control, allowing you to set priorities as you wish (to our knowledge, few competitors have something equivalent in place). Obviously, we can help with many other essay types such as critical thinking essay, argumentative essay, etc. In particular, the research paper definition article on our website highlights a few popular paper types we work with.

Another unique advantage is that we allow and encourage you to communicate directly with your writer (if you wish) guiding his or her work – feel free to request partial drafts, to clarify potential issues you worry about, or even to revise papers as often as needed (for free) until you achieve a satisfactory result. We’ve implemented a system where money is released to writers only after students are fully satisfied with what they get. If you feel like giving it a try, it’s easy and worry-free! Just follow the link below.

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How to Write a Summary | Guide & Examples

How to Write a Summary | Guide & Examples

Published on 25 September 2022 by Shona McCombes . Revised on 12 May 2023.

Summarising , or writing a summary, means giving a concise overview of a text’s main points in your own words. A summary is always much shorter than the original text.

There are five key steps that can help you to write a summary:

Read the text
Break it down into sections
Identify the key points in each section
Write the summary
Check the summary against the article

Writing a summary does not involve critiquing or analysing the source. You should simply provide an accurate account of the most important information and ideas (without copying any text from the original).

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When to write a summary, step 1: read the text, step 2: break the text down into sections, step 3: identify the key points in each section, step 4: write the summary, step 5: check the summary against the article, frequently asked questions.

There are many situations in which you might have to summarise an article or other source:

As a stand-alone assignment to show you’ve understood the material
To keep notes that will help you remember what you’ve read
To give an overview of other researchers’ work in a literature review

When you’re writing an academic text like an essay , research paper , or dissertation , you’ll integrate sources in a variety of ways. You might use a brief quote to support your point, or paraphrase a few sentences or paragraphs.

But it’s often appropriate to summarize a whole article or chapter if it is especially relevant to your own research, or to provide an overview of a source before you analyse or critique it.

In any case, the goal of summarising is to give your reader a clear understanding of the original source. Follow the five steps outlined below to write a good summary.

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You should read the article more than once to make sure you’ve thoroughly understood it. It’s often effective to read in three stages:

Scan the article quickly to get a sense of its topic and overall shape.
Read the article carefully, highlighting important points and taking notes as you read.
Skim the article again to confirm you’ve understood the key points, and reread any particularly important or difficult passages.

There are some tricks you can use to identify the key points as you read:

Start by reading the abstract . This already contains the author’s own summary of their work, and it tells you what to expect from the article.
Pay attention to headings and subheadings . These should give you a good sense of what each part is about.
Read the introduction and the conclusion together and compare them: What did the author set out to do, and what was the outcome?

To make the text more manageable and understand its sub-points, break it down into smaller sections.

If the text is a scientific paper that follows a standard empirical structure, it is probably already organised into clearly marked sections, usually including an introduction, methods, results, and discussion.

Other types of articles may not be explicitly divided into sections. But most articles and essays will be structured around a series of sub-points or themes.

Now it’s time go through each section and pick out its most important points. What does your reader need to know to understand the overall argument or conclusion of the article?

Keep in mind that a summary does not involve paraphrasing every single paragraph of the article. Your goal is to extract the essential points, leaving out anything that can be considered background information or supplementary detail.

In a scientific article, there are some easy questions you can ask to identify the key points in each part.

Key points of a scientific article
Introduction	or problem was addressed? formulated?
Methods
Results
Discussion/conclusion

If the article takes a different form, you might have to think more carefully about what points are most important for the reader to understand its argument.

In that case, pay particular attention to the thesis statement —the central claim that the author wants us to accept, which usually appears in the introduction—and the topic sentences that signal the main idea of each paragraph.

Now that you know the key points that the article aims to communicate, you need to put them in your own words.

To avoid plagiarism and show you’ve understood the article, it’s essential to properly paraphrase the author’s ideas. Do not copy and paste parts of the article, not even just a sentence or two.

The best way to do this is to put the article aside and write out your own understanding of the author’s key points.

Examples of article summaries

Let’s take a look at an example. Below, we summarise this article , which scientifically investigates the old saying ‘an apple a day keeps the doctor away’.

An article summary like the above would be appropriate for a stand-alone summary assignment. However, you’ll often want to give an even more concise summary of an article.

For example, in a literature review or research paper, you may want to briefly summarize this study as part of a wider discussion of various sources. In this case, we can boil our summary down even further to include only the most relevant information.

Citing the source you’re summarizing

When including a summary as part of a larger text, it’s essential to properly cite the source you’re summarizing. The exact format depends on your citation style , but it usually includes an in-text citation and a full reference at the end of your paper.

You can easily create your citations and references in APA or MLA using our free citation generators.

APA Citation Generator MLA Citation Generator

Finally, read through the article once more to ensure that:

You’ve accurately represented the author’s work
You haven’t missed any essential information
The phrasing is not too similar to any sentences in the original.

If you’re summarising many articles as part of your own work, it may be a good idea to use a plagiarism checker to double-check that your text is completely original and properly cited. Just be sure to use one that’s safe and reliable.

A summary is a short overview of the main points of an article or other source, written entirely in your own words.

Save yourself some time with the free summariser.

A summary is always much shorter than the original text. The length of a summary can range from just a few sentences to several paragraphs; it depends on the length of the article you’re summarising, and on the purpose of the summary.

With the summariser tool you can easily adjust the length of your summary.

You might have to write a summary of a source:

As a stand-alone assignment to prove you understand the material
For your own use, to keep notes on your reading
To provide an overview of other researchers’ work in a literature review
In a paper , to summarise or introduce a relevant study

To avoid plagiarism when summarising an article or other source, follow these two rules:

Write the summary entirely in your own words by paraphrasing the author’s ideas.
Reference the source with an in-text citation and a full reference so your reader can easily find the original text.

An abstract concisely explains all the key points of an academic text such as a thesis , dissertation or journal article. It should summarise the whole text, not just introduce it.

An abstract is a type of summary , but summaries are also written elsewhere in academic writing . For example, you might summarise a source in a paper , in a literature review , or as a standalone assignment.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2023, May 12). How to Write a Summary | Guide & Examples. Scribbr. Retrieved 3 September 2024, from https://www.scribbr.co.uk/working-sources/how-to-write-a-summary/

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Writing a Summary – Explanation & Examples

Published by Alvin Nicolas at October 17th, 2023 , Revised On October 17, 2023

In a world bombarded with vast amounts of information, condensing and presenting data in a digestible format becomes invaluable. Enter summaries.

A summary is a brief and concise account of the main points of a larger body of work. It distils complex ideas, narratives, or data into a version that is quicker to read and easier to understand yet still retains the essence of the original content.

Importance of Summaries

The importance of summarising extends far beyond just making reading more manageable. In academic settings, summaries aid students in understanding and retaining complex materials, from textbook chapters to research articles. They also serve as tools to showcase one’s grasp of the subject in essays and reports.

In professional arenas, summaries are pivotal in business reports, executive briefings, and even emails where key points need to be conveyed quickly to decision-makers. Meanwhile, summarising skills come into play in our personal lives when we relay news stories to friends, recap a movie plot, or even scroll through condensed news or app notifications on our smartphones.

Why Do We Write Summaries?

In our modern information age, the sheer volume of content available can be overwhelming. From detailed research papers to comprehensive news articles, the quest for knowledge is often met with lengthy and complex resources. This is where the power of a well-crafted summary comes into play. But what drives us to create or seek out summaries? Let’s discuss.

Makes Important Things Easy to Remember

At the heart of summarisation is the goal to understand. A well-written summary aids in digesting complex material. By distilling larger works into their core points, we reinforce the primary messages, making them easier to remember. This is especially crucial for students who need to retain knowledge for exams or professionals prepping for a meeting based on a lengthy report.

Simplification of Complex Topics

Not everyone is an expert in every field. Often, topics come laden with jargon, intricate details, and nuanced arguments. Summaries act as a bridge, translating this complexity into accessible and straightforward content. This is especially beneficial for individuals new to a topic or those who need just the highlights without the intricacies.

Aid in Researching and Understanding Diverse Sources

Researchers, writers, and academics often wade through many sources when working on a project. This involves finding sources of different types, such as primary or secondary sources , and then understanding their content. Sifting through each source in its entirety can be time-consuming. Summaries offer a streamlined way to understand each source’s main arguments or findings, making synthesising information from diverse materials more efficient.

Condensing Information for Presentation or Sharing

In professional settings, there is often a need to present findings, updates, or recommendations to stakeholders. An executive might not have the time to go through a 50-page report, but they would certainly appreciate a concise summary highlighting the key points. Similarly, in our personal lives, we often summarise movie plots, book stories, or news events when sharing with friends or family.

Characteristics of a Good Summary

Crafting an effective summary is an art. It’s more than just shortening a piece of content; it is about capturing the essence of the original work in a manner that is both accessible and true to its intent. Let’s explore the primary characteristics that distinguish a good summary from a mediocre one:

Conciseness

At the core of a summary is the concept of brevity. But being concise doesn’t mean leaving out vital information. A good summary will:

Eliminate superfluous details or repetitive points.
Focus on the primary arguments, events, or findings.
Use succinct language without compromising the message.

Objectivity

Summarising is not about infusing personal opinions or interpretations. A quality summary will:

Stick to the facts as presented in the original content.
Avoid introducing personal biases or perspectives.
Represent the original author’s intent faithfully.

A summary is meant to simplify and make content accessible. This is only possible if the summary itself is easy to understand. Ensuring clarity involves:

Avoiding jargon or technical terms unless they are essential to the content. If they are used, they should be clearly defined.
Structuring sentences in a straightforward manner.
Making sure ideas are presented in a way that even someone unfamiliar with the topic can grasp the primary points.

A jumble of ideas, no matter how concise, will not make for a good summary. Coherence ensures that there’s a logical flow to the summarised content. A coherent summary will:

Maintain a logical sequence, often following the structure of the original content.
Use transition words or phrases to connect ideas and ensure smooth progression.
Group related ideas together to provide structure and avoid confusion.

Steps of Writing a Summary

The process of creating a compelling summary is not merely about cutting down content. It involves understanding, discerning, and crafting. Here is a step-by-step guide to writing a summary that encapsulates the essence of the original work:

Reading Actively

Engage deeply with the content to ensure a thorough understanding.

Read the entire document or work first to grasp its overall intent and structure.
On the second read, underline or highlight the standout points or pivotal moments.
Make brief notes in the margins or on a separate sheet, capturing the core ideas in your own words.

Identifying the Main Idea

Determine the backbone of the content, around which all other details revolve.

Ask yourself: “What is the primary message or theme the author wants to convey?”
This can often be found in the title, introduction, or conclusion of a piece.
Frame the main idea in a clear and concise statement to guide your summary.

List Key Supporting Points

Understand the pillars that uphold the main idea, providing evidence or depth to the primary message.

Refer back to the points you underlined or highlighted during your active reading.
Note major arguments, evidence, or examples that the author uses to back up the main idea.
Prioritise these points based on their significance to the main idea.

Draft the Summary

Convert your understanding into a condensed, coherent version of the original.

Start with a statement of the main idea.
Follow with the key supporting points, maintaining logical order.
Avoid including trivial details or examples unless they’re crucial to the primary message.
Use your own words, ensuring you are not plagiarising the original content.

Fine-tune your draft to ensure clarity, accuracy, and brevity.

Read your draft aloud to check for flow and coherence.
Ensure that your summary remains objective, avoiding any personal interpretations or biases.
Check the length. See if any non-essential details can be removed without sacrificing understanding if it is too lengthy.
Ensure clarity by ensuring the language is straightforward, and the main ideas are easily grasped.

The research done by our experts have:

Precision and Clarity
Zero Plagiarism
Authentic Sources

Dos and Don’ts of Summarising Key Points

Summarising, while seemingly straightforward, comes with its nuances. Properly condensing content demands a balance between brevity and fidelity to the original work. To aid in crafting exemplary summaries, here is a guide on the essential dos and don’ts:

Use your Own Words

This ensures that you have truly understood the content and are not merely parroting it. It also prevents issues of plagiarism.

Tip: After reading the original content, take a moment to reflect on it. Then, without looking at the source, write down the main points in your own words.

Attribute Sources Properly

Giving credit is both ethical and provides context to readers, helping them trace back to the original work if needed. How to cite sources correctly is a skill every writer should master.

Tip: Use signal phrases like “According to [Author/Source]…” or “As [Author/Source] points out…” to seamlessly incorporate attributions.

Ensure Accuracy of the Summarised Content

A summary should be a reliable reflection of the original content. Distorting or misrepresenting the original ideas compromises the integrity of the summary.

Tip: After drafting your summary, cross-check with the original content to ensure all key points are represented accurately and ensure you are referencing credible sources .

Avoid Copy-Pasting Chunks of Original Content

This not only raises plagiarism concerns but also shows a lack of genuine engagement with the material.

Tip: If a particular phrase or sentence from the original is pivotal and cannot be reworded without losing its essence, use block quotes , quotation marks, and attribute the source.

Do not Inject your Personal Opinion

A summary should be an objective reflection of the source material. Introducing personal biases or interpretations can mislead readers.

Tip: Stick to the facts and arguments presented in the original content. If you find yourself writing “I think” or “In my opinion,” reevaluate the sentence.

Do not Omit Crucial Information

While a summary is meant to be concise, it shouldn’t be at the expense of vital details that are essential to understanding the original content’s core message.

Tip: Prioritise information. Always include the main idea and its primary supports. If you are unsure whether a detail is crucial, consider its impact on the overall message.

Examples of Summaries

Here are a few examples that will help you get a clearer view of how to write a summary.

Example 1: Summary of a News Article

Original Article: The article reports on the recent discovery of a rare species of frog in the Amazon rainforest. The frog, named the “Emerald Whisperer” due to its unique green hue and the soft chirping sounds it makes, was found by a team of researchers from the University of Texas. The discovery is significant as it offers insights into the biodiversity of the region, and the Emerald Whisperer might also play a pivotal role in understanding the ecosystem balance.

Summary: Researchers from the University of Texas have discovered a unique frog, termed the “Emerald Whisperer,” in the Amazon rainforest. This finding sheds light on the region’s biodiversity and underscores the importance of the frog in ecological studies.

Example 2: Summary of a Research Paper

Original Paper: In a study titled “The Impact of Urbanisation on Bee Populations,” researchers conducted a year-long observation on bee colonies in three urban areas and three rural areas. Using specific metrics like colony health, bee productivity, and population size, the study found that urban environments saw a 30% decline in bee populations compared to rural settings. The research attributes this decline to factors like pollution, reduced green spaces, and increased temperatures in urban areas.

Summary: A study analysing the effects of urbanisation on bee colonies found a significant 30% decrease in bee populations in urban settings compared to rural areas. The decline is linked to urban factors such as pollution, diminished greenery, and elevated temperatures.

Example 3: Summary of a Novel

Original Story: In the novel “Winds of Fate,” protagonist Clara is trapped in a timeless city where memories dictate reality. Throughout her journey, she encounters characters from her past, present, and imagined future. Battling her own perceptions and a menacing shadow figure, Clara seeks an elusive gateway to return to her real world. In the climax, she confronts the shadow, which turns out to be her own fear, and upon overcoming it, she finds her way back, realising that reality is subjective.

Summary: “Winds of Fate” follows Clara’s adventures in a surreal city shaped by memories. Confronting figures from various phases of her life and battling a symbolic shadow of her own fear, Clara eventually discovers that reality’s perception is malleable and subjective.

Frequently Asked Questions

How long is a summary.

A summary condenses a larger piece of content, capturing its main points and essence. It is usually one-fourth of the original content.

What is a summary?

A summary is a concise representation of a larger text or content, highlighting its main ideas and points. It distils complex information into a shorter form, allowing readers to quickly grasp the essence of the original material without delving into extensive details. Summaries prioritise clarity, brevity, and accuracy.

When should I write a summary?

Write a summary when you need to condense lengthy content for easier comprehension and recall. It’s useful in academic settings, professional reports, presentations, and research to highlight key points. Summaries aid in comparing multiple sources, preparing for discussions, and sharing essential details of extensive materials efficiently with others.

How can I summarise a source without plagiarising?

To summarise without plagiarising: Read the source thoroughly, understand its main ideas, and then write the summary in your own words. Avoid copying phrases verbatim. Attribute the source properly. Use paraphrasing techniques and cross-check your summary against the original to ensure distinctiveness while retaining accuracy. Always prioritise understanding over direct replication.

What is the difference between a summary and an abstract?

A summary condenses a text, capturing its main points from various content types like books, articles, or movies. An abstract, typically found in research papers and scientific articles, provides a brief overview of the study’s purpose, methodology, results, and conclusions. Both offer concise versions, but abstracts are more structured and specific.

Article Summaries, Reviews & Critiques

Writing an article summary.

Writing an article REVIEW
Writing an article CRITIQUE
Citing Sources This link opens in a new window
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When writing a summary, the goal is to compose a concise and objective overview of the original article. The summary should focus only on the article's main ideas and important details that support those ideas.

Guidelines for summarizing an article:

State the main ideas.
Identify the most important details that support the main ideas.
Summarize in your own words.
Do not copy phrases or sentences unless they are being used as direct quotations.
Express the underlying meaning of the article, but do not critique or analyze.
The summary should be about one third the length of the original article.

Your summary should include:

Give an overview of the article, including the title and the name of the author.
Provide a thesis statement that states the main idea of the article.
Use the body paragraphs to explain the supporting ideas of your thesis statement.
One-paragraph summary - one sentence per supporting detail, providing 1-2 examples for each.
Multi-paragraph summary - one paragraph per supporting detail, providing 2-3 examples for each.
Start each paragraph with a topic sentence.
Use transitional words and phrases to connect ideas.
Summarize your thesis statement and the underlying meaning of the article.

Adapted from "Guidelines for Using In-Text Citations in a Summary (or Research Paper)" by Christine Bauer-Ramazani, 2020

Additional Resources

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How to Write a Summary - Guide & Examples (from Scribbr.com)

Writing a Summary (from The University of Arizona Global Campus Writing Center)

Next: Writing an article REVIEW >>
Last Updated: Mar 15, 2024 9:32 AM
URL: https://libguides.randolph.edu/summaries

How to Write a Research Paper Summary

Journal submission: Tips to submit better manuscripts | Paperpal

One of the most important skills you can imbibe as an academician is to know how to summarize a research paper. During your academic journey, you may need to write a summary of findings in research quite often and for varied reasons – be it to write an introduction for a peer-reviewed publication , to submit a critical review, or to simply create a useful database for future referencing.

It can be quite challenging to effectively write a research paper summary for often complex work, which is where a pre-determined workflow can help you optimize the process. Investing time in developing this skill can also help you improve your scientific acumen, increasing your efficiency and productivity at work. This article illustrates some useful advice on how to write a research summary effectively. But, what is research summary in the first place?

A research paper summary is a crisp, comprehensive overview of a research paper, which encapsulates the purpose, findings, methods, conclusions, and relevance of a study. A well-written research paper summary is an indicator of how well you have understood the author’s work.

Draft a research paper summary in minutes with paperpal. click here to start writing.

2. Invest enough time to understand the topic deeply

Use Paperpal to summarize your research paper. Click here to get started!

Mistakes to avoid while writing your research paper summary

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Frequently asked questions (faq), how to write a research paper summary.

Writing a good research paper summary comes with practice and skill. Here is some useful advice on how to write a research paper summary effectively.

1. Determine the focus of your summary

Before you begin to write a summary of research papers, determine the aim of your research paper summary. This will give you more clarity on how to summarize a research paper, including what to highlight and where to find the information you need, which accelerates the entire process. If you are aiming for the summary to be a supporting document or a proof of principle for your current research findings, then you can look for elements that are relevant to your work.

On the other hand, if your research summary is intended to be a critical review of the research article, you may need to use a completely different lens while reading the paper and conduct your own research regarding the accuracy of the data presented. Then again, if the research summary is intended to be a source of information for future referencing, you will likely have a different approach. This makes determining the focus of your summary a key step in the process of writing an effective research paper summary.

2. Invest enough time to understand the topic deeply

In order to author an effective research paper summary, you need to dive into the topic of the research article. Begin by doing a quick scan for relevant information under each section of the paper. The abstract is a great starting point as it helps you to quickly identify the top highlights of the research article, speeding up the process of understanding the key findings in the paper. Be sure to do a careful read of the research paper, preparing notes that describe each section in your own words to put together a summary of research example or a first draft. This will save your time and energy in revisiting the paper to confirm relevant details and ease the entire process of writing a research paper summary.

When reading papers, be sure to acknowledge and ignore any pre-conceived notions that you might have regarding the research topic. This will not only help you understand the topic better but will also help you develop a more balanced perspective, ensuring that your research paper summary is devoid of any personal opinions or biases.

3. Keep the summary crisp, brief and engaging

A research paper summary is usually intended to highlight and explain the key points of any study, saving the time required to read through the entire article. Thus, your primary goal while compiling the summary should be to keep it as brief, crisp and readable as possible. Usually, a short introduction followed by 1-2 paragraphs is adequate for an effective research article summary. Avoid going into too much technical detail while describing the main results and conclusions of the study. Rather focus on connecting the main findings of the study to the hypothesis , which can make the summary more engaging. For example, instead of simply reporting an original finding – “the graph showed a decrease in the mortality rates…”, you can say, “there was a decline in the number of deaths, as predicted by the authors while beginning the study…” or “there was a decline in the number of deaths, which came as a surprise to the authors as this was completely unexpected…”.

Unless you are writing a critical review of the research article, the language used in your research paper summaries should revolve around reporting the findings, not assessing them. On the other hand, if you intend to submit your summary as a critical review, make sure to provide sufficient external evidence to support your final analysis. Invest sufficient time in editing and proofreading your research paper summary thoroughly to ensure you’ve captured the findings accurately. You can also get an external opinion on the preliminary draft of the research paper summary from colleagues or peers who have not worked on the research topic.

Mistakes to avoid while writing your research paper summary

Now that you’ve understood how to summarize a research paper, watch out for these red flags while writing your summary.

Not paying attention to the word limit and recommended format, especially while submitting a critical review
Evaluating the findings instead of maintaining an objective , unbiased view while reading the research paper
Skipping the essential editing step , which can help eliminate avoidable errors and ensure that the language does not misrepresent the findings
Plagiarism, it is critical to write in your own words or paraphrase appropriately when reporting the findings in your scientific article summary

We hope the recommendations listed above will help answer the question of how to summarize a research paper and enable you to tackle the process effectively.

Summarize your research paper with Paperpal

Paperpal, an AI academic writing assistant, is designed to support academics at every step of the academic writing process. Built on over two decades of experience helping researchers get published and trained on millions of published research articles, Paperpal offers human precision at machine speed. Paperpal Copilot, with advanced generative AI features, can help academics achieve 2x the writing in half the time, while transforming how they research and write.

How to summarize a research paper with Paperpal?

To generate your research paper summary, simply login to the platform and use the Paperpal Copilot Summary feature to create a flawless summary of your work. Here’s a step-by-step process to help you craft a summary in minutes:

Paste relevant research articles to be summarized into Paperpal; the AI will scan each section and extract key information.
In minutes, Paperpal will generate a comprehensive summary that showcases the main paper highlights while adhering to academic writing conventions.
Check the content to polish and refine the language, ensure your own voice, and add citations or references as needed.

The abstract and research paper summary serve similar purposes but differ in scope, length, and placement. The abstract is a concise yet detailed overview of the research, placed at the beginning of a paper, with the aim of providing readers with a quick understanding of the paper’s content and to help them decide whether to read the full article. Usually limited to a few hundred words, it highlights the main objectives, methods, results, and conclusions of the study. On the other hand, a research paper summary provides a crisp account of the entire research paper. Its purpose is to provide a brief recap for readers who may want to quickly grasp the main points of the research without reading the entire paper in detail.

The structure of a research summary can vary depending on the specific requirements or guidelines provided by the target publication or institution. A typical research summary includes the following key sections: introduction (including the research question or objective), methodology (briefly describing the research design and methods), results (summarizing the key findings), discussion (highlighting the implications and significance of the findings), and conclusion (providing a summary of the main points and potential future directions).

The summary of a research paper is important because it provides a condensed overview of the study’s purpose, methods, results, and conclusions. It allows you to quickly grasp the main points and relevance of the research without having to read the entire paper. Research summaries can also be an invaluable way to communicate research findings to a broader audience, such as policymakers or the general public.

When writing a research paper summary, it is crucial to avoid plagiarism by properly attributing the original authors’ work. To learn how to summarize a research paper while avoiding plagiarism, follow these critical guidelines: (1) Read the paper thoroughly to understand the main points and key findings. (2) Use your own words and sentence structures to restate the information, ensuring that the research paper summary reflects your understanding of the paper. (3) Clearly indicate when you are paraphrasing or quoting directly from the original paper by using appropriate citation styles. (4) Cite the original source for any specific ideas, concepts, or data that you include in your summary. (5) Review your summary to ensure it accurately represents the research paper while giving credit to the original authors.

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How to write a summary of a research paper (with template)

by Diana Ribeiro Last updated Jul 20, 2020 | Published on Jun 27, 2020 Writing Skills 0 comments

In our daily work as medical writers, we have to read many scholarly articles and extract the main information from them. Having a process to retrieve that information and create a short summary that you can easily access will save you precious time. That’s why I decided to guide you through my process of summarising a research article and created a handy template.

Having short summaries of academic papers is useful to create news articles, press releases, social media posts, blog articles, or curated news reports, like the one I write weekly for my newsletter subscribers .

What’s the importance of summarising research articles?

If you don’t have a system to extract the main information from a scholarly paper, you may have to re-read it repeatedly, looking for that piece of information you know it’s there. Sure, you can use a highlighter pen to mark the main points, but sometimes what happens is that you end up with yellow walls of text. Or green. Or even a rainbow. Which may be pretty, but it’s quite useless as a retrieval system.

What also happens when you highlight text is that you end up with a diverse array of writing styles, none of them being your own. This way, when you try to write a text with information from multiple sources, you have to search for the information and write it in a consistent style.

In this article, I’ll show you how to retrieve the most relevant information from a scientific paper, how to write it in a compelling way, and how to present it in a news-worthy style that’s easily adaptable to your audience. Ready?

Three steps to summarise a research paper

1. scan and extract the main points.

First things first, so you have to read the paper. But that doesn’t mean you have to read it from start to finish. Start by scanning the article for its main points.

Here’s the essential information to extract from the research paper you have in front of you:

Authors, year, doi
Study question: look in the introduction for a phrase like “the aim of this study was”
Hypothesis tested
Study methods: design, participants, materials, procedure, what was manipulated (independent variables), what was measured (dependent variables), how data were analysed.
Findings: from the results section; fill this before you look at the discussion section, if possible. Write bullet points.
Interpretation: how did the authors interpreted their findings? Use short sentences, in your own words.

After extracting the key information , revisit the article and read it more attentively, to see if you missed something. Add some notes to your summary, but take care to avoid plagiarism. Write notes in your own words. If you can’t do that at this moment, use quotation marks to indicate that your note came straight from the study. You can rewrite it later, when you have a better grasp of the study.

2. Use a journalistic approach for the first draft

Some sources advise you to keep the same structure as the scientific article, but I like to use the journalistic approach of news articles and flush out the more relevant information first, followed by the details. This is more enticing for readers, making them want to continue reading. Yes, I know that your reader may be just you, but I know I have lost myself in some of the things I’ve written, so…keep it interesting, even for a future self 😊.

This is the main information you have to put together:

Title of the article: I like to keep the original article title for the summary, because it’s easier to refer back to the original article if I need to. Sometimes I add a second title, just for me, if the article title is too obscure or long.

1 st paragraph: Answer the 5 W’s in 3-4 sentences.

Who? (the authors)

What? (main finding)

When and where? (journal, date of publication)

Why? (relevance)

This should be a standalone paragraph, meaning that the reader should be able to take out the main information even if they just read this paragraph.

Subsequent paragraphs: In 2-3 paragraphs or less, provide context and more information about the research done. If you’re not sure if a detail is important or not, you can include it here and edit it out in the next step.

3. Polish the rough edges

In this stage, you’re going to make a quick edit, checking for completeness and accuracy. Make sure you’ve included all the main points without repeating yourself. Double-check all the numbers. Stay focused on the research questions to avoid tangents. Avoid using jargon and the passive voice whenever possible.

Final summary

Using this approach, you’ll end up with a short summary of your article that you can use to craft other types of writing, such as press releases, news articles, social media blurbs, and many others.

The advantages of summarising research articles are that you can better understand what the article is about, and you’ll have a text written by you, so it’s easier to adapt and you avoid unintentional plagiarism.

That’s it! My guide to write a research paper summary 😊

I’ve created a handout with all the information in this blog post plus a fill-in-the-blanks template that you can use to summarise research articles, you can download it using the form below. You’ll be signed up to my mailing list, and receive a weekly roundup of news in the biomedical industry as a bonus!

If you have any comments or questions, please let me know in the comment box below.

Get the research paper summary template

And subscribe to the biopharma newsletter 🙂

Unsubscribe anytime.

Check your confirmation email for the template.

About Diana Ribeiro

Diana Ribeiro is a pharmacist and freelance medical writer based in Cascais, Portugal. Before starting her career in medical writing, Diana worked 10+ years in hospital and community pharmacies, where she helped patients and healthcare professionals with drug management and information. Nowadays, she helps pharma, biotech, and meddev companies communicate with their audiences in a clear, accurate, and compelling way. Diana is an active member of the European Medical Writers Association, where she volunteers for the webinar team. You can find more about her on LinkedIn .

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How to Write a Summary of a Research Paper

Last Updated: July 10, 2020 References

This article was reviewed by Annaliese Dunne and by wikiHow staff writer, Hannah Madden . Annaliese Dunne is a Middle School English Teacher. With over 10 years of teaching experience, her areas of expertise include writing and grammar instruction, as well as teaching reading comprehension. She is also an experienced freelance writer. She received her Bachelor's degree in English. This article has been viewed 28,782 times.

Writing a summary of an academic research paper is an important skill, and it shows that you understand all of the relevant information presented to you. However, writing a summary can be tough, since it requires you to be completely objective and keep any analysis or criticisms to yourself. By keeping your goal in mind as you read the paper and focusing on the key points, you can write a succinct, accurate summary of a research paper to prove that you understood the overall conclusion.

Reading the Research Paper

Step 1 Figure out the focus of your summary.

For instance, if you’re supporting an argument in your own research paper, focus on the elements that are similar to yours.
Or, if you’re comparing and contrasting methodology, focus on the methods and the significance of the results.

Step 2 Scan through the article to pick out important information.

You can also read the abstract of the paper as a good example of what the authors find to be important in their article.

Step 3 Read the article fully 1 to 2 times.

Depending on how long and dense the paper is, your initial reading could take you up to an hour or more.

Step 4 Underline or highlight important information.

The important information will usually be toward the end of the paper as the authors explain their findings and conclusions.

Step 5 Take notes summarizing sections in your own words.

Writing a summary without plagiarizing, or copying the paper, is really important. Writing notes in your own words will help you get into the mindset of relaying information in your own way.

Including Relevant Information

Step 1 Aim to report the findings, not evaluate them.

For example, “The methods used in this paper are not up to standards and require more testing to be conclusive.” is an analysis.
”The methods used in this paper include an in-depth survey and interview session with each candidate.” is a summary.

If you’re writing a summary for class, your professor may specify how long your summary should be.
Some summaries can even be as short as one sentence.

Step 3 State the research question and hypothesis.

”Environmental conditions in North Carolina pose a threat to frogs and toads.”

Step 4 Describe the testing and analyzation methods.

For example: “According to the climate model, frog and toad populations have been decreasing at a rapid rate over the past 10 years, and are on track to decrease even further in the coming years.”

Step 5 Talk about the results and how significant they were.

For example: “Smith and Herman (2008) argue that by decreasing greenhouse gases, frog and toad populations could reach historical levels within 20 years, and the climate model projections support that statement.”
You can add in the authors and year of publication at any time during your summary.

Step 6 Edit your summary for accuracy and flow.

If you have time, try reading your summary to someone who hasn’t read the original paper and see if they understand the key points of the article.

Expert Q&A

Make sure you fully understand the paper before you start writing the summary. Thanks Helpful 2 Not Helpful 0
Plagiarism can have serious consequences in the academic world, so make sure you’re writing your summary in your own words. [12] X Research source Thanks Helpful 0 Not Helpful 0

↑ https://writingcenter.uconn.edu/wp-content/uploads/sites/593/2014/06/How_to_Summarize_a_Research_Article1.pdf
↑ https://www.ufv.ca/media/assets/academic-success-centre/handouts/Summarizing-a-Scholarly-Journal-Article-rev2018.pdf
↑ https://integrity.mit.edu/handbook/academic-writing/summarizing
↑ https://writingcenter.unc.edu/tips-and-tools/summary-using-it-wisely/
↑ https://davidson.libguides.com/c.php?g=349327&p=2361763

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12 Best Tools For Perfect Research Summary Writing

Discover the 12 best tools to streamline your research summary writing, ensuring clarity and precision every time.

Aug 29, 2024

person making new notes - Research Summary

Consider you finally find the time to tackle that research paper for your class. You pull up your literature search and see dozens of articles and studies staring back at you. As you scroll through the titles and abstracts, you realize you need to figure out how to summarize the research to get started on your paper.

Writing a practical research summary can feel daunting, but it doesn’t have to. In this guide, we’ll break down what a research summary is, why it’s essential, and how to write one. This information lets you confidently write your research summary and finish your paper.

Otio’s AI research and writing partner can help you write efficient research summaries and papers. Our tool can summarize academic articles so you can understand the material and finish your writing.

What is a research summary, purpose of a research summary, how do you write a research summary in 10 simple steps, what is a phd research summary, examples of research summary, supercharge your researching ability with otio — try otio for free today.

man with notes infront of him - Research Summary

A research summary is a piece of writing that summarizes your research on a specific topic. Its primary goal is to offer the reader a detailed study overview with critical findings. A research summary generally contains the structure of the article.

You must know the goal of your analysis before you launch a project. A research overview summarizes the detailed response and highlights particular issues. Writing it may be troublesome. You want to start with a structure in mind to write a good overview.

1. Read The Entire Research Paper

Before writing a research summary , you must read and understand the entire research paper. This may seem like a time-consuming task, but it is essential to write a good summary. Make sure you know the paper's main points before you begin writing.

2. Take Notes As You Read

As you read, take notes on the main points of the paper. These notes will come in handy when you are writing your summary. Be sure to note any necessary information, such as the main conclusions of the author's writing. This helpful tip will also help you write a practical blog summary in less time.

3. Organize Your Thoughts

Once you have finished reading and taking notes on the paper, it is time to start writing your summary. Before you begin, take a few minutes to organize your thoughts. Write down the main points that you want to include in your summary. Then, arrange these points in a logical order.

4. Write The Summary

Now that you have organized your thoughts, it is time to start writing the summary. Begin by stating the author’s thesis statement or main conclusion. Then, briefly describe each of the main points from the paper. Be sure to write clearly and concisely. When you finish, reread your summary to ensure it accurately reflects the paper's content.

5. Write The Introduction

After you have written the summary, it is time to write the introduction. The introduction should include an overview of the paper and a summary description. It should also state the main idea.

6. Introduce The Report's Purpose

The summary of a research paper should include a brief description of the paper's purpose. It should state the paper's thesis statement and briefly describe each of the main points of the paper.

7. Use Keywords To Introduce The Report

When introducing the summary of a research paper, use keywords familiar to the reader. This will help them understand the summary and why it is essential.

8. State The Author's Conclusions

The summary of a research paper should include a brief statement of the author's conclusions. This will help your teacher understand what the paper is trying to achieve.

9. Keep It Concise

A summary should be concise and to the point. It should not include any new information or arguments. It should be one paragraph long at maximum.

10. Edit And Proofread

After you have written the summary, edit and proofread it to ensure it is accurate and precise. This will help ensure that your summary is effective and free of any grammar or spelling errors.

person using top tools - Research Summary

1. Otio: Your AI Research Assistant

Knowledge workers, researchers, and students today need help with content overload and are left to deal with it using fragmented, complex, and manual tooling. Too many settle for stitching together complicated bookmarking, read-it-later, and note-taking apps to get through their workflows. Now that anyone can create content with a button, this problem will only worsen. Otio solves this problem by providing researchers with one AI-native workspace. It helps them:

1. Collect a wide range of data sources, from bookmarks, tweets, and extensive books to YouTube videos.

2. extract key takeaways with detailed ai-generated notes and source-grounded q&a chat. , 3. create draft outputs using the sources you’ve collected. .

Otio helps you to go from a reading list to the first draft faster. Along with this, Otio also enables you to write research papers/essays faster. Here are our top features that researchers love: AI-generated notes on all bookmarks (Youtube videos, PDFs, articles, etc.), Otio enables you to chat with individual links or entire knowledge bases, just like you chat with ChatGPT, as well as AI-assisted writing.

Let Otio be your AI research and writing partner — try Otio for free today !

2. Hypotenuse AI: The Versatile Summarizer

Like all the AI text summarizers on this list, Hypotenuse AI can take the input text and generate a short summary. One area where it stands out is its ability to handle various input options: You can simply copy-paste the text, directly upload a PDF, or even drop a YouTube link to create summaries.

You can summarize nearly 200,000 characters (or 50,000 words) at once.

Hypotenuse AI summarizes articles, PDFs, paragraphs, documents, and videos.

With the AI tool, you can create engaging hooks and repurpose content for social media.

You'll need a paid plan after the 7-day free trial.

There needs to be a free plan available.

The AI tool majorly focuses on generating eCommerce and marketing content.

3. Scalenut: The Beginner-Friendly AI Summarizer

Scalenut is one of the powerful AI text summarizers for beginners or anyone starting out. While it's not as polished as some other business-focused apps, it's significantly easier to use — and the output is just as good as others. If you want a basic online text summarizer that lets you summarize the notes within 800 characters (not words), Scalenut is your app.

With Scalenut, you get a dedicated summary generation tool for more granular control.

The keyword planner available helps build content directly from the short and sweet summaries.

The AI tool integrates well with a whole suite of SEO tools, making it a more SEO-focused summarizer.

You only get to generate one summary per day.

Scalenut's paid plans are expensive compared to other AI tools.

You must summarize long-form articles or blogs at most the limit of 800 characters.

4. SciSummary: The Academic AI Summarizer

SciSummary is an AI summarizer that helps summarize single or multiple research papers. It combines and compares the content summaries from research papers, article links, etc.

It can save time and effort for scientists, students, and enthusiasts who want to keep up with the latest scientific developments.

It can provide accurate and digestible summaries powered by advanced AI models that learn from feedback and expert guidance.

It can help users read between the lines and understand complex scientific texts' main points and implications.

It may only capture some nuances and details of the original articles or papers, which may be necessary for some purposes or audiences.

Some types of scientific texts, such as highly technical, specialized, or interdisciplinary, may require more domain knowledge or context.

Some sources of scientific information, such as websites, videos, or podcasts not in text format, may need help summarizing.

5. Quillbot: The AI Summarizer for Academic Papers

QuillBot uses advanced neural network models to summarize research papers accurately and effectively. The tool leverages cutting-edge technology to condense lengthy papers into concise and informative summaries, making it easier for users to navigate vast amounts of literature.

You can upload the text for summarization directly from a document.

It's excellent for summarizing essays, papers, and lengthy documents.

You can summarize long texts up to 1200 words for free.

The free plan is limited to professionals.

There could have been some more output types.

QuillBot's Premium plan only gives you 6000 words for summaries per month.

6. Scribbr: The Research Paper Assistant

Scribbr is an AI-driven academic writing assistant with a summarization feature tailored for research papers. The tool assists users in the research paper writing process by summarizing and condensing information from various sources, offering support in structuring and organizing content effectively.

7. TLDR This: The Online Article Summarizer

TLDR This uses advanced AI to effectively filter out unimportant arguments from online articles and provide readers only with vital takeaways. Its streamlined interface eliminates ads and distractions while summarizing key points, metadata, images, and other crucial article details.

TLDR This condenses even very lengthy materials into compact summaries users can quickly consume, making it easier to process a vast range of internet content efficiently.

Ten free "AI" summaries

Summarization of long text

Basic summary extraction

Premium option cost

No significant improvement in premium options

8. AI Summarizer: The Text Document Summarizer

AI Summarizer harnesses artificial intelligence to summarize research papers and other text documents automatically. The tool streamlines the summarization process, making it efficient and accurate, enabling users to extract essential information from extensive research papers efficiently.

Easy-to-understand interface

1500-word limit

Multiple language support

Contains advertisements

Requires security captcha completion

Struggles with lengthy content summarization

9. Jasper: The Advanced Summarizer

Jasper AI is a robust summarizing tool that helps users generate AI-powered paper summaries quickly and effectively. The tool supports the prompt creation of premium-quality summaries, assisting researchers in distilling complex information into concise and informative outputs.

Jasper offers some advanced features, like generating a text from scratch and even summarizing it.

It integrates well with third-party apps like Surfer, Grammarly, and its own AI art generator.

It's versatile and can be used to create summaries of blogs, articles, website copy, emails, and even social media posts.

There's no free plan available — though you get a 7-day free trial.

You'll need to have a flexible budget to use Jasper AI.

The Jasper app has a steep learning curve.

10. Resoomer: The Summary Extractor

Resoomer rapidly analyzes textual documents to determine the essential sentences and summarizes these key points using its proprietary semantic analysis algorithm.

By automatically identifying what information matters most, Resoomer can condense elaborate texts across diverse subjects into brief overviews of their core message. With swift copy-and-paste functionality requiring no signup, this specialized tool simplifies the reading experience by extracting only vital details from complex writings.

Clear and accurate summaries

Creative sentence combining

Variety of modes and options

Lengthy text summarization without word limit in premium mode

Confusing interface with irrelevant features

Long-winded summaries spread across multiple pages

11. Anyword: The Marketing-Focused Summarizer

When I saw Anyword's summary, I could easily state that the content was unique and worth sharing, making this AI tool an excellent choice for marketers. Plus, it's very easy to use.

Once you've copied-pasted the text and chosen a summary type, paragraph, keywords, or TL;DR, it generates a summary in minutes. Approve it; you can share the text directly without worrying about plagiarized content.

You can test the AI tool with the 7-day free trial.

The Anyword's text generator and summarizer are perfect for creating long-form pieces like blog posts with snippets.

You can give detailed prompts to the AI tool to customize the generated text.

Any word is expensive for a more limited set of features than other AI summarizers.

It can sometimes be slower to use.

There is no free Anyword plan available.

12. Frase: The SEO Summarizer

Frase is a powerful AI-powered summarizer that focuses on SEO. This means it can generate summaries that attract audiences and rank higher. Its proprietary model stands out, providing more flexibility, competitive pricing, and custom features.

Frase uses BLUF and Reverse Pyramid techniques to generate summaries, improving ranking chances.

It's free to use Frase's summary generator.

Instead of GPT-3.5 or GPT-4, Frase uses its proprietary model.

There's no way to add links to the blog or article to generate a summary.

You can input up to 600-700 words for summarization.

It might not be an ideal article summarizer for those who don't care about SEO.

A research summary for a PhD is called a research statement . The research statement (or statement of research interests) is included in academic job applications. It summarizes your research accomplishments, current work, and future direction and potential. The statement can discuss specific issues such as funding history and potential requirements for laboratory equipment and space and other resources, possible research and industrial collaborations, and how your research contributes to your field's future research direction.

The research statement should be technical but intelligible to all department members, including those outside your subdiscipline. So keep the “big picture” in mind. The strongest research statements present a readable, compelling, and realistic research agenda that fits well with the department's needs, facilities, and goals. Research statements can be weakened by: overly ambitious proposals lack of apparent direction lack of big-picture focus, and inadequate attention to the needs and facilities of the department or position.

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Research Summary Example 1: A Look at the Probability of an Unexpected Volcanic Eruption in Yellowstone

Introduction .

If the Yellowstone supervolcano erupted massively , the consequences would be catastrophic for the United States. The importance of analyzing the likelihood of such an eruption cannot be overstated.

Hypothesis

An eruption of the Yellowstone supervolcano would be preceded by intense precursory activity manifesting a few weeks up to a few years in advance.

Results

Statistical data from multiple volcanic eruptions happening worldwide show activity that preceded these events (in particular, how early each type of activity was detected).

Discussion and Conclusion

Given that scientists continuously monitor Yellowstone and that signs of an eruption are normally detected much in advance, at least a few days in advance, the hypothesis is confirmed. This could be applied to creating emergency plans detailing an organized evacuation campaign and other response measures.

Research Summary Example 2: The Frequency of Extreme Weather Events in the US from 2000-2008 as Compared to the ‘50s

Weather events bring immense material damage and cause human victims.

Extreme weather events are significantly more frequent nowadays than in the ‘50s.

Several categories of extreme events occur regularly now and then: droughts and associated fires, massive rainfall/snowfall and associated floods, hurricanes, tornadoes, Arctic cold waves, etc.

Discussion and Conclusion

Several extreme events have become significantly more frequent recently, confirming this hypothesis. This increasing frequency correlates reliably with rising CO2 levels in the atmosphere and growing temperatures worldwide.

In the absence of another recent significant global change that could explain a higher frequency of disasters, and knowing how growing temperature disturbs weather patterns, it is natural to assume that global warming (CO2) causes this increase in frequency. This, in turn, suggests that this increased frequency of disasters is not a short-term phenomenon but is here to stay until we address CO2 levels.

Researchers, students, and knowledge workers have long struggled with the initial stages of research projects. The early steps of gathering and organizing information , taking notes, and synthesizing the material into a coherent summary are vital for establishing a solid foundation for any research endeavor.

These steps can be tedious, overwhelming, and time-consuming. Otio streamlines this process so you can go from the reading list to the first draft faster. Along with this, Otio also helps you write research papers/essays faster. Here are our top features that researchers love:

AI-generated notes on all bookmarks (Youtube videos, PDFs, articles, etc.), Otio enables you to chat with individual links or entire knowledge bases, just like you chat with ChatGPT, as well as AI-assisted writing.

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How to Create a Structured Research Paper Outline | Example

Published on August 7, 2022 by Courtney Gahan . Revised on August 15, 2023.

A research paper outline is a useful tool to aid in the writing process , providing a structure to follow with all information to be included in the paper clearly organized.

A quality outline can make writing your research paper more efficient by helping to:

Organize your thoughts
Understand the flow of information and how ideas are related
Ensure nothing is forgotten

A research paper outline can also give your teacher an early idea of the final product.

Instantly correct all language mistakes in your text

Upload your document to correct all your mistakes in minutes

Research paper outline example, how to write a research paper outline, formatting your research paper outline, language in research paper outlines.

Definition of measles
Rise in cases in recent years in places the disease was previously eliminated or had very low rates of infection
Figures: Number of cases per year on average, number in recent years. Relate to immunization
Symptoms and timeframes of disease
Risk of fatality, including statistics
How measles is spread
Immunization procedures in different regions
Different regions, focusing on the arguments from those against immunization
Immunization figures in affected regions
High number of cases in non-immunizing regions
Illnesses that can result from measles virus
Fatal cases of other illnesses after patient contracted measles
Summary of arguments of different groups
Summary of figures and relationship with recent immunization debate
Which side of the argument appears to be correct?

Scribbr Citation Checker New

The AI-powered Citation Checker helps you avoid common mistakes such as:

Missing commas and periods
Incorrect usage of “et al.”
Ampersands (&) in narrative citations
Missing reference entries

Follow these steps to start your research paper outline:

Decide on the subject of the paper
Write down all the ideas you want to include or discuss
Organize related ideas into sub-groups
Arrange your ideas into a hierarchy: What should the reader learn first? What is most important? Which idea will help end your paper most effectively?
Create headings and subheadings that are effective
Format the outline in either alphanumeric, full-sentence or decimal format

There are three different kinds of research paper outline: alphanumeric, full-sentence and decimal outlines. The differences relate to formatting and style of writing.

Alphanumeric
Full-sentence

An alphanumeric outline is most commonly used. It uses Roman numerals, capitalized letters, arabic numerals, lowercase letters to organize the flow of information. Text is written with short notes rather than full sentences.

Sub-point of sub-point 1

Essentially the same as the alphanumeric outline, but with the text written in full sentences rather than short points.

Additional sub-point to conclude discussion of point of evidence introduced in point A

A decimal outline is similar in format to the alphanumeric outline, but with a different numbering system: 1, 1.1, 1.2, etc. Text is written as short notes rather than full sentences.

1.1.1 Sub-point of first point
1.1.2 Sub-point of first point
1.2 Second point

To write an effective research paper outline, it is important to pay attention to language. This is especially important if it is one you will show to your teacher or be assessed on.

There are four main considerations: parallelism, coordination, subordination and division.

Parallelism: Be consistent with grammatical form

Parallel structure or parallelism is the repetition of a particular grammatical form within a sentence, or in this case, between points and sub-points. This simply means that if the first point is a verb , the sub-point should also be a verb.

Example of parallelism:

Include different regions, focusing on the different arguments from those against immunization

Coordination: Be aware of each point’s weight

Your chosen subheadings should hold the same significance as each other, as should all first sub-points, secondary sub-points, and so on.

Example of coordination:

Include immunization figures in affected regions
Illnesses that can result from the measles virus

Subordination: Work from general to specific

Subordination refers to the separation of general points from specific. Your main headings should be quite general, and each level of sub-point should become more specific.

Example of subordination:

Division: break information into sub-points.

Your headings should be divided into two or more subsections. There is no limit to how many subsections you can include under each heading, but keep in mind that the information will be structured into a paragraph during the writing stage, so you should not go overboard with the number of sub-points.

Ready to start writing or looking for guidance on a different step in the process? Read our step-by-step guide on how to write a research paper .

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Gahan, C. (2023, August 15). How to Create a Structured Research Paper Outline | Example. Scribbr. Retrieved September 3, 2024, from https://www.scribbr.com/research-paper/outline/

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Research Paper Summary

Ai generator.

Whether you are a student, an academic scholar, or even working in business, there is no denying that a research paper summary is the one tool that you are going to expect when it comes to writing your research paper or research studies. There is also no denying how useful the summary is going to be when you have to report it to your superiors or your professors without having to go through the entire research paper. Students know for themselves that writing a summary of their research paper is useful. With that, here are examples of research paper summaries to download.

10+ Research Paper Summary Examples

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What Is a Research Paper Summary?

Research paper summaries are short but descriptive writings that are expected in a research paper . What goes in a research paper summary is the main topic or the main plot of your research paper. However, what is and should never be included are any new discoveries, arguments and new leads that help your research. The purpose of the summary is to simply give out the general point of view or the outline of your research paper and nothing else. This is often the mistake made by students when they think of a research paper summary. The need to add all new leads to help their research in the summary. The only main thing to focus on your summary is the overview and the general outline .

How to Write a Research Paper Summary

Being able to write a research paper summary is important and quite a useful skill. As this does not only work for students on their research paper, but it also works for employees who are given the task to write a project summary. It basically works just the same. To get a glimpse of what you can do to make your research paper summary, here are simple steps you can follow.

Step 1: Take the Main Part of Your Research

When you make your summary, the first paragraph will mainly be about your research paper. The first part is to take the main part of your research. The main part or the main topic should be what it is about. Make sure what you are writing is what your research paper is about, as there are times when your topic may not be the main goal of your paper.

Step 2: Break It Down to Smaller Topics

Since the first paragraph is focused on the introduction and the main topic, the second paragraph will focus mainly on breaking down your main or general topic into smaller subtopics. By doing this, it is easier for you to divide and explain every single important detail of your research paper. Students are often tasked to do this in order for them to get a better outlook of their research paper and how they are able to piece together the smaller topics to the main topic.

Step 3: Get the Gist

The third and final paragraph will be the gist of your research paper. This includes the heart or the main part, the findings and the conclusion. The gist has to be a general summary of your research paper. It should have the facts that support it, the findings of your research and the hypothesis. Add in your conclusion at the end.

Step 4: Proofread Your Work

Lastly, make sure to proofread your entire research paper summary. This is just to make sure you did not misspell any words, your punctuations are in the correct place and the tone of your writing fits the paper you are making.

What is a research paper summary?

Research paper summaries are short but descriptive writings that are expected in a research paper. What goes in a research paper summary is the main topic or the main plot of your research paper.

What are the characteristics of a research paper summary?

The characteristics of a research paper summary are the following:

The introduction and the main topic
The breaking of the main topic to sub topics
The gist of the research paper summary
The conclusion

How lengthy can a research paper summary be?

The normal length of a research paper summary should not exceed more than a page. However, when it comes to the number of words for a summary, your wording should not exceed the maximum number of four hundred words.

When it comes to writing a research paper, there is no denying that you must also write a summary for it. Since a research paper can sometimes be overwhelming to those who will be listening to you talk about it, you can relieve it by making a summary of your paper. This will also help them follow what you are discussing and what it is about.

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Reading Research Effectively
Narrowing a Topic Idea
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An executive summary is a thorough overview of a research report or other type of document that synthesizes key points for its readers, saving them time and preparing them to understand the study's overall content. It is a separate, stand-alone document of sufficient detail and clarity to ensure that the reader can completely understand the contents of the main research study. An executive summary can be anywhere from 1-10 pages long depending on the length of the report, or it can be the summary of more than one document [e.g., papers submitted for a group project].

Bailey, Edward, P. The Plain English Approach to Business Writing . (New York: Oxford University Press, 1997), p. 73-80 Todorovic, Zelimir William and Marietta Wolczacka Frye. “Writing Effective Executive Summaries: An Interdisciplinary Examination.” In United States Association for Small Business and Entrepreneurship. Conference Proceedings . (Decatur, IL: United States Association for Small Business and Entrepreneurship, 2009): pp. 662-691.

Importance of a Good Executive Summary

Although an executive summary is similar to an abstract in that they both summarize the contents of a research study, there are several key differences. With research abstracts, the author's recommendations are rarely included, or if they are, they are implicit rather than explicit. Recommendations are generally not stated in academic abstracts because scholars operate in a discursive environment, where debates, discussions, and dialogs are meant to precede the implementation of any new research findings. The conceptual nature of much academic writing also means that recommendations arising from the findings are distributed widely and not easily or usefully encapsulated. Executive summaries are used mainly when a research study has been developed for an organizational partner, funding entity, or other external group that participated in the research . In such cases, the research report and executive summary are often written for policy makers outside of academe, while abstracts are written for the academic community. Professors, therefore, assign the writing of executive summaries so students can practice synthesizing and writing about the contents of comprehensive research studies for external stakeholder groups.

When preparing to write, keep in mind that:

An executive summary is not an abstract.
An executive summary is not an introduction.
An executive summary is not a preface.
An executive summary is not a random collection of highlights.

Christensen, Jay. Executive Summaries Complete The Report. California State University Northridge; Clayton, John. "Writing an Executive Summary that Means Business." Harvard Management Communication Letter (July 2003): 2-4; Keller, Chuck. "Stay Healthy with a Winning Executive Summary." Technical Communication 41 (1994): 511-517; Murphy, Herta A., Herbert W. Hildebrandt, and Jane P. Thomas. Effective Business Communications . New York: McGraw-Hill, 1997; Vassallo, Philip. "Executive Summaries: Where Less Really is More." ETC.: A Review of General Semantics 60 (Spring 2003): 83-90 .

Structure and Writing Style

Writing an Executive Summary

Read the Entire Document This may go without saying, but it is critically important that you read the entire research study thoroughly from start to finish before you begin to write the executive summary. Take notes as you go along, highlighting important statements of fact, key findings, and recommended courses of action. This will better prepare you for how to organize and summarize the study. Remember this is not a brief abstract of 300 words or less but, essentially, a mini-paper of your paper, with a focus on recommendations.

Isolate the Major Points Within the Original Document Choose which parts of the document are the most important to those who will read it. These points must be included within the executive summary in order to provide a thorough and complete explanation of what the document is trying to convey.

Separate the Main Sections Closely examine each section of the original document and discern the main differences in each. After you have a firm understanding about what each section offers in respect to the other sections, write a few sentences for each section describing the main ideas. Although the format may vary, the main sections of an executive summary likely will include the following:

An opening statement, with brief background information,
The purpose of research study,
Method of data gathering and analysis,
Overview of findings, and,
A description of each recommendation, accompanied by a justification. Note that the recommendations are sometimes quoted verbatim from the research study.

Combine the Information Use the information gathered to combine them into an executive summary that is no longer than 10% of the original document. Be concise! The purpose is to provide a brief explanation of the entire document with a focus on the recommendations that have emerged from your research. How you word this will likely differ depending on your audience and what they care about most. If necessary, selectively incorporate bullet points for emphasis and brevity. Re-read your Executive Summary After you've completed your executive summary, let it sit for a while before coming back to re-read it. Check to make sure that the summary will make sense as a separate document from the full research study. By taking some time before re-reading it, you allow yourself to see the summary with fresh, unbiased eyes.

Common Mistakes to Avoid

Length of the Executive Summary As a general rule, the correct length of an executive summary is that it meets the criteria of no more pages than 10% of the number of pages in the original document, with an upper limit of no more than ten pages [i.e., ten pages for a 100 page document]. This requirement keeps the document short enough to be read by your audience, but long enough to allow it to be a complete, stand-alone synopsis. Cutting and Pasting With the exception of specific recommendations made in the study, do not simply cut and paste whole sections of the original document into the executive summary. You should paraphrase information from the longer document. Avoid taking up space with excessive subtitles and lists, unless they are absolutely necessary for the reader to have a complete understanding of the original document. Consider the Audience Although unlikely to be required by your professor, there is the possibility that more than one executive summary will have to be written for a given document [e.g., one for policy-makers, one for private industry, one for philanthropists]. This may only necessitate the rewriting of the introduction and conclusion, but it could require rewriting the entire summary in order to fit the needs of the reader. If necessary, be sure to consider the types of audiences who may benefit from your study and make adjustments accordingly. Clarity in Writing One of the biggest mistakes you can make is related to the clarity of your executive summary. Always note that your audience [or audiences] are likely seeing your research study for the first time. The best way to avoid a disorganized or cluttered executive summary is to write it after the study is completed. Always follow the same strategies for proofreading that you would for any research paper. Use Strong and Positive Language Don’t weaken your executive summary with passive, imprecise language. The executive summary is a stand-alone document intended to convince the reader to make a decision concerning whether to implement the recommendations you make. Once convinced, it is assumed that the full document will provide the details needed to implement the recommendations. Although you should resist the temptation to pad your summary with pleas or biased statements, do pay particular attention to ensuring that a sense of urgency is created in the implications, recommendations, and conclusions presented in the executive summary. Be sure to target readers who are likely to implement the recommendations.

Bailey, Edward, P. The Plain English Approach to Business Writing . (New York: Oxford University Press, 1997), p. 73-80; Christensen, Jay. Executive Summaries Complete The Report. California State University Northridge; Executive Summaries. Writing@CSU. Colorado State University; Clayton, John. "Writing an Executive Summary That Means Business." Harvard Management Communication Letter , 2003; Executive Summary. University Writing Center. Texas A&M University; Green, Duncan. Writing an Executive Summary. Oxfam’s Research Guidelines series ; Guidelines for Writing an Executive Summary. Astia.org; Markowitz, Eric. How to Write an Executive Summary. Inc. Magazine, September, 15, 2010; Kawaski, Guy. The Art of the Executive Summary. "How to Change the World" blog; Keller, Chuck. "Stay Healthy with a Winning Executive Summary." Technical Communication 41 (1994): 511-517; The Report Abstract and Executive Summary. The Writing Lab and The OWL. Purdue University; Writing Executive Summaries. Effective Writing Center. University of Maryland; Kolin, Philip. Successful Writing at Work . 10th edition. (Boston, MA: Cengage Learning, 2013), p. 435-437; Moral, Mary. "Writing Recommendations and Executive Summaries." Keeping Good Companies 64 (June 2012): 274-278; Todorovic, Zelimir William and Marietta Wolczacka Frye. “Writing Effective Executive Summaries: An Interdisciplinary Examination.” In United States Association for Small Business and Entrepreneurship. Conference Proceedings . (Decatur, IL: United States Association for Small Business and Entrepreneurship, 2009): pp. 662-691.

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Published: 28 August 2024

AI generates covertly racist decisions about people based on their dialect

Valentin Hofmann ORCID: orcid.org/0000-0001-6603-3428 1 , 2 , 3 ,
Pratyusha Ria Kalluri 4 ,
Dan Jurafsky ORCID: orcid.org/0000-0002-6459-7745 4 &
Sharese King 5

Nature volume 633 , pages 147–154 ( 2024 ) Cite this article

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Computer science

Hundreds of millions of people now interact with language models, with uses ranging from help with writing 1 , 2 to informing hiring decisions 3 . However, these language models are known to perpetuate systematic racial prejudices, making their judgements biased in problematic ways about groups such as African Americans 4 , 5 , 6 , 7 . Although previous research has focused on overt racism in language models, social scientists have argued that racism with a more subtle character has developed over time, particularly in the United States after the civil rights movement 8 , 9 . It is unknown whether this covert racism manifests in language models. Here, we demonstrate that language models embody covert racism in the form of dialect prejudice, exhibiting raciolinguistic stereotypes about speakers of African American English (AAE) that are more negative than any human stereotypes about African Americans ever experimentally recorded. By contrast, the language models’ overt stereotypes about African Americans are more positive. Dialect prejudice has the potential for harmful consequences: language models are more likely to suggest that speakers of AAE be assigned less-prestigious jobs, be convicted of crimes and be sentenced to death. Finally, we show that current practices of alleviating racial bias in language models, such as human preference alignment, exacerbate the discrepancy between covert and overt stereotypes, by superficially obscuring the racism that language models maintain on a deeper level. Our findings have far-reaching implications for the fair and safe use of language technology.

Large language models propagate race-based medicine

The benefits, risks and bounds of personalizing the alignment of large language models to individuals

Cognitive causes of ‘like me’ race and gender biases in human language production

Language models are a type of artificial intelligence (AI) that has been trained to process and generate text. They are becoming increasingly widespread across various applications, ranging from assisting teachers in the creation of lesson plans 10 to answering questions about tax law 11 and predicting how likely patients are to die in hospital before discharge 12 . As the stakes of the decisions entrusted to language models rise, so does the concern that they mirror or even amplify human biases encoded in the data they were trained on, thereby perpetuating discrimination against racialized, gendered and other minoritized social groups 4 , 5 , 6 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 .

Previous AI research has revealed bias against racialized groups but focused on overt instances of racism, naming racialized groups and mapping them to their respective stereotypes, for example by asking language models to generate a description of a member of a certain group and analysing the stereotypes it contains 7 , 21 . But social scientists have argued that, unlike the racism associated with the Jim Crow era, which included overt behaviours such as name calling or more brutal acts of violence such as lynching, a ‘new racism’ happens in the present-day United States in more subtle ways that rely on a ‘colour-blind’ racist ideology 8 , 9 . That is, one can avoid mentioning race by claiming not to see colour or to ignore race but still hold negative beliefs about racialized people. Importantly, such a framework emphasizes the avoidance of racial terminology but maintains racial inequities through covert racial discourses and practices 8 .

Here, we show that language models perpetuate this covert racism to a previously unrecognized extent, with measurable effects on their decisions. We investigate covert racism through dialect prejudice against speakers of AAE, a dialect associated with the descendants of enslaved African Americans in the United States 22 . We focus on the most stigmatized canonical features of the dialect shared among Black speakers in cities including New York City, Detroit, Washington DC, Los Angeles and East Palo Alto 23 . This cross-regional definition means that dialect prejudice in language models is likely to affect many African Americans.

Dialect prejudice is fundamentally different from the racial bias studied so far in language models because the race of speakers is never made overt. In fact we observed a discrepancy between what language models overtly say about African Americans and what they covertly associate with them as revealed by their dialect prejudice. This discrepancy is particularly pronounced for language models trained with human feedback (HF), such as GPT4: our results indicate that HF training obscures the racism on the surface, but the racial stereotypes remain unaffected on a deeper level. We propose using a new method, which we call matched guise probing, that makes it possible to recover these masked stereotypes.

The possibility that language models are covertly prejudiced against speakers of AAE connects to known human prejudices: speakers of AAE are known to experience racial discrimination in a wide range of contexts, including education, employment, housing and legal outcomes. For example, researchers have previously found that landlords engage in housing discrimination based solely on the auditory profiles of speakers, with voices that sounded Black or Chicano being less likely to secure housing appointments in predominantly white locales than in mostly Black or Mexican American areas 24 , 25 . Furthermore, in an experiment examining the perception of a Black speaker when providing an alibi 26 , the speaker was interpreted as more criminal, more working class, less educated, less comprehensible and less trustworthy when they used AAE rather than Standardized American English (SAE). Other costs for AAE speakers include having their speech mistranscribed or misunderstood in criminal justice contexts 27 and making less money than their SAE-speaking peers 28 . These harms connect to themes in broader racial ideology about African Americans and stereotypes about their intelligence, competence and propensity to commit crimes 29 , 30 , 31 , 32 , 33 , 34 , 35 . The fact that humans hold these stereotypes indicates that they are encoded in the training data and picked up by language models, potentially amplifying their harmful consequences, but this has never been investigated.

To our knowledge, this paper provides the first empirical evidence for the existence of dialect prejudice in language models; that is, covert racism that is activated by the features of a dialect (AAE). Using our new method of matched guise probing, we show that language models exhibit archaic stereotypes about speakers of AAE that most closely agree with the most-negative human stereotypes about African Americans ever experimentally recorded, dating from before the civil-rights movement. Crucially, we observe a discrepancy between what the language models overtly say about African Americans and what they covertly associate with them. Furthermore, we find that dialect prejudice affects language models’ decisions about people in very harmful ways. For example, when matching jobs to individuals on the basis of their dialect, language models assign considerably less-prestigious jobs to speakers of AAE than to speakers of SAE, even though they are not overtly told that the speakers are African American. Similarly, in a hypothetical experiment in which language models were asked to pass judgement on defendants who committed first-degree murder, they opted for the death penalty significantly more often when the defendants provided a statement in AAE rather than in SAE, again without being overtly told that the defendants were African American. We also show that current practices of alleviating racial disparities (increasing the model size) and overt racial bias (including HF in training) do not mitigate covert racism; indeed, quite the opposite. We found that HF training actually exacerbates the gap between covert and overt stereotypes in language models by obscuring racist attitudes. Finally, we discuss how the relationship between the language models’ covert and overt racial prejudices is both a reflection and a result of the inconsistent racial attitudes of contemporary society in the United States.

Probing AI dialect prejudice

To explore how dialect choice impacts the predictions that language models make about speakers in the absence of other cues about their racial identity, we took inspiration from the ‘matched guise’ technique used in sociolinguistics, in which subjects listen to recordings of speakers of two languages or dialects and make judgements about various traits of those speakers 36 , 37 . Applying the matched guise technique to the AAE–SAE contrast, researchers have shown that people identify speakers of AAE as Black with above-chance accuracy 24 , 26 , 38 and attach racial stereotypes to them, even without prior knowledge of their race 39 , 40 , 41 , 42 , 43 . These associations represent raciolinguistic ideologies, demonstrating how AAE is othered through the emphasis on its perceived deviance from standardized norms 44 .

Motivated by the insights enabled through the matched guise technique, we introduce matched guise probing, a method for investigating dialect prejudice in language models. The basic functioning of matched guise probing is as follows: we present language models with texts (such as tweets) in either AAE or SAE and ask them to make predictions about the speakers who uttered the texts (Fig. 1 and Methods ). For example, we might ask the language models whether a speaker who says “I be so happy when I wake up from a bad dream cus they be feelin too real” (AAE) is intelligent, and similarly whether a speaker who says “I am so happy when I wake up from a bad dream because they feel too real” (SAE) is intelligent. Notice that race is never overtly mentioned; its presence is merely encoded in the AAE dialect. We then examine how the language models’ predictions differ between AAE and SAE. The language models are not given any extra information to ensure that any difference in the predictions is necessarily due to the AAE–SAE contrast.

a , We used texts in SAE (green) and AAE (blue). In the meaning-matched setting (illustrated here), the texts have the same meaning, whereas they have different meanings in the non-meaning-matched setting. b , We embedded the SAE and AAE texts in prompts that asked for properties of the speakers who uttered the texts. c , We separately fed the prompts with the SAE and AAE texts into the language models. d , We retrieved and compared the predictions for the SAE and AAE inputs, here illustrated by five adjectives from the Princeton Trilogy. See Methods for more details.

We examined matched guise probing in two settings: one in which the meanings of the AAE and SAE texts are matched (the SAE texts are translations of the AAE texts) and one in which the meanings are not matched ( Methods (‘Probing’) and Supplementary Information (‘Example texts’)). Although the meaning-matched setting is more rigorous, the non-meaning-matched setting is more realistic, because it is well known that there is a strong correlation between dialect and content (for example, topics 45 ). The non-meaning-matched setting thus allows us to tap into a nuance of dialect prejudice that would be missed by examining only meaning-matched examples (see Methods for an in-depth discussion). Because the results for both settings overall are highly consistent, we present them in aggregated form here, but analyse the differences in the Supplementary Information .

We examined GPT2 (ref. 46 ), RoBERTa 47 , T5 (ref. 48 ), GPT3.5 (ref. 49 ) and GPT4 (ref. 50 ), each in one or more model versions, amounting to a total of 12 examined models ( Methods and Supplementary Information (‘Language models’)). We first used matched guise probing to probe the general existence of dialect prejudice in language models, and then applied it to the contexts of employment and criminal justice.

Covert stereotypes in language models

We started by investigating whether the attitudes that language models exhibit about speakers of AAE reflect human stereotypes about African Americans. To do so, we replicated the experimental set-up of the Princeton Trilogy 29 , 30 , 31 , 34 , a series of studies investigating the racial stereotypes held by Americans, with the difference that instead of overtly mentioning race to the language models, we used matched guise probing based on AAE and SAE texts ( Methods ).

Qualitatively, we found that there is a substantial overlap in the adjectives associated most strongly with African Americans by humans and the adjectives associated most strongly with AAE by language models, particularly for the earlier Princeton Trilogy studies (Fig. 2a ). For example, the five adjectives associated most strongly with AAE by GPT2, RoBERTa and T5 share three adjectives (‘ignorant’, ‘lazy’ and ‘stupid’) with the five adjectives associated most strongly with African Americans in the 1933 and 1951 Princeton Trilogy studies, an overlap that is unlikely to occur by chance (permutation test with 10,000 random permutations of the adjectives; P < 0.01). Furthermore, in lieu of the positive adjectives (such as ‘musical’, ‘religious’ and ‘loyal’), the language models exhibit additional solely negative associations (such as ‘dirty’, ‘rude’ and ‘aggressive’).

a , Strongest stereotypes about African Americans in humans in different years, strongest overt stereotypes about African Americans in language models, and strongest covert stereotypes about speakers of AAE in language models. Colour coding as positive (green) and negative (red) is based on ref. 34 . Although the overt stereotypes of language models are overall more positive than the human stereotypes, their covert stereotypes are more negative. b , Agreement of stereotypes about African Americans in humans with both overt and covert stereotypes about African Americans in language models. The black dotted line shows chance agreement using a random bootstrap. Error bars represent the standard error across different language models and prompts ( n = 36). The language models’ overt stereotypes agree most strongly with current human stereotypes, which are the most positive experimentally recorded ones, but their covert stereotypes agree most strongly with human stereotypes from the 1930s, which are the most negative experimentally recorded ones. c , Stereotype strength for individual linguistic features of AAE. Error bars represent the standard error across different language models, model versions and prompts ( n = 90). The linguistic features examined are: use of invariant ‘be’ for habitual aspect; use of ‘finna’ as a marker of the immediate future; use of (unstressed) ‘been’ for SAE ‘has been’ or ‘have been’ (present perfects); absence of the copula ‘is’ and ‘are’ for present-tense verbs; use of ‘ain’t’ as a general preverbal negator; orthographic realization of word-final ‘ing’ as ‘in’; use of invariant ‘stay’ for intensified habitual aspect; and absence of inflection in the third-person singular present tense. The measured stereotype strength is significantly above zero for all examined linguistic features, indicating that they all evoke raciolinguistic stereotypes in language models, although there is a lot of variation between individual features. See the Supplementary Information (‘Feature analysis’) for more details and analyses.

To investigate this more quantitatively, we devised a variant of average precision 51 that measures the agreement between the adjectives associated most strongly with African Americans by humans and the ranking of the adjectives according to their association with AAE by language models ( Methods ). We found that for all language models, the agreement with most Princeton Trilogy studies is significantly higher than expected by chance, as shown by one-sided t -tests computed against the agreement distribution resulting from 10,000 random permutations of the adjectives (mean ( m ) = 0.162, standard deviation ( s ) = 0.106; Extended Data Table 1 ); and that the agreement is particularly pronounced for the stereotypes reported in 1933 and falls for each study after that, almost reaching the level of chance agreement for 2012 (Fig. 2b ). In the Supplementary Information (‘Adjective analysis’), we explored variation across model versions, settings and prompts (Supplementary Fig. 2 and Supplementary Table 4 ).

To explain the observed temporal trend, we measured the average favourability of the top five adjectives for all Princeton Trilogy studies and language models, drawing from crowd-sourced ratings for the Princeton Trilogy adjectives on a scale between −2 (very negative) and 2 (very positive; see Methods , ‘Covert-stereotype analysis’). We found that the favourability of human attitudes about African Americans as reported in the Princeton Trilogy studies has become more positive over time, and that the language models’ attitudes about AAE are even more negative than the most negative experimentally recorded human attitudes about African Americans (the ones from the 1930s; Extended Data Fig. 1 ). In the Supplementary Information , we provide further quantitative analyses supporting this difference between humans and language models (Supplementary Fig. 7 ).

Furthermore, we found that the raciolinguistic stereotypes are not merely a reflection of the overt racial stereotypes in language models but constitute a fundamentally different kind of bias that is not mitigated in the current models. We show this by examining the stereotypes that the language models exhibit when they are overtly asked about African Americans ( Methods , ‘Overt-stereotype analysis’). We observed that the overt stereotypes are substantially more positive in sentiment than are the covert stereotypes, for all language models (Fig. 2a and Extended Data Fig. 1 ). Strikingly, for RoBERTa, T5, GPT3.5 and GPT4, although their covert stereotypes about speakers of AAE are more negative than the most negative experimentally recorded human stereotypes, their overt stereotypes about African Americans are more positive than the most positive experimentally recorded human stereotypes. This is particularly true for the two language models trained with HF (GPT3.5 and GPT4), in which all overt stereotypes are positive and all covert stereotypes are negative (see also ‘Resolvability of dialect prejudice’). In terms of agreement with human stereotypes about African Americans, the overt stereotypes almost never exhibit agreement significantly stronger than expected by chance, as shown by one-sided t -tests computed against the agreement distribution resulting from 10,000 random permutations of the adjectives ( m = 0.162, s = 0.106; Extended Data Table 2 ). Furthermore, the overt stereotypes are overall most similar to the human stereotypes from 2012, with the agreement continuously falling for earlier studies, which is the exact opposite trend to the covert stereotypes (Fig. 2b ).

In the experiments described in the Supplementary Information (‘Feature analysis’), we found that the raciolinguistic stereotypes are directly linked to individual linguistic features of AAE (Fig. 2c and Supplementary Table 14 ), and that a higher density of such linguistic features results in stronger stereotypical associations (Supplementary Fig. 11 and Supplementary Table 13 ). Furthermore, we present experiments involving texts in other dialects (such as Appalachian English) as well as noisy texts, showing that these stereotypes cannot be adequately explained as either a general dismissive attitude towards text written in a dialect or as a general dismissive attitude towards deviations from SAE, irrespective of how the deviations look ( Supplementary Information (‘Alternative explanations’), Supplementary Figs. 12 and 13 and Supplementary Tables 15 and 16 ). Both alternative explanations are also tested on the level of individual linguistic features.

Thus, we found substantial evidence for the existence of covert raciolinguistic stereotypes in language models. Our experiments show that these stereotypes are similar to the archaic human stereotypes about African Americans that existed before the civil rights movement, are even more negative than the most negative experimentally recorded human stereotypes about African Americans, and are both qualitatively and quantitatively different from the previously reported overt racial stereotypes in language models, indicating that they are a fundamentally different kind of bias. Finally, our analyses demonstrate that the detected stereotypes are inherently linked to AAE and its linguistic features.

Impact of covert racism on AI decisions

To determine what harmful consequences the covert stereotypes have in the real world, we focused on two areas in which racial stereotypes about speakers of AAE and African Americans have been repeatedly shown to bias human decisions: employment and criminality. There is a growing impetus to use AI systems in these areas. Indeed, AI systems are already being used for personnel selection 52 , 53 , including automated analyses of applicants’ social-media posts 54 , 55 , and technologies for predicting legal outcomes are under active development 56 , 57 , 58 . Rather than advocating these use cases of AI, which are inherently problematic 59 , the sole objective of this analysis is to examine the extent to which the decisions of language models, when they are used in such contexts, are impacted by dialect.

First, we examined decisions about employability. Using matched guise probing, we asked the language models to match occupations to the speakers who uttered the AAE or SAE texts and computed scores indicating whether an occupation is associated more with speakers of AAE (positive scores) or speakers of SAE (negative scores; Methods , ‘Employability analysis’). The average score of the occupations was negative ( m = –0.046, s = 0.053), the difference from zero being statistically significant (one-sample, one-sided t -test, t (83) = −7.9, P < 0.001). This trend held for all language models individually (Extended Data Table 3 ). Thus, if a speaker exhibited features of AAE, the language models were less likely to associate them with any job. Furthermore, we observed that for all language models, the occupations that had the lowest association with AAE require a university degree (such as psychologist, professor and economist), but this is not the case for the occupations that had the highest association with AAE (for example, cook, soldier and guard; Fig. 3a ). Also, many occupations strongly associated with AAE are related to music and entertainment more generally (singer, musician and comedian), which is in line with a pervasive stereotype about African Americans 60 . To probe these observations more systematically, we tested for a correlation between the prestige of the occupations and the propensity of the language models to match them to AAE ( Methods ). Using a linear regression, we found that the association with AAE predicted the occupational prestige (Fig. 3b ; β = −7.8, R 2 = 0.193, F (1, 63) = 15.1, P < 0.001). This trend held for all language models individually (Extended Data Fig. 2 and Extended Data Table 4 ), albeit in a less pronounced way for GPT3.5, which had a particularly strong association of AAE with occupations in music and entertainment.

a , Association of different occupations with AAE or SAE. Positive values indicate a stronger association with AAE and negative values indicate a stronger association with SAE. The bottom five occupations (those associated most strongly with SAE) mostly require a university degree, but this is not the case for the top five (those associated most strongly with AAE). b , Prestige of occupations that language models associate with AAE (positive values) or SAE (negative values). The shaded area shows a 95% confidence band around the regression line. The association with AAE or SAE predicts the occupational prestige. Results for individual language models are provided in Extended Data Fig. 2 . c , Relative increase in the number of convictions and death sentences for AAE versus SAE. Error bars represent the standard error across different model versions, settings and prompts ( n = 24 for GPT2, n = 12 for RoBERTa, n = 24 for T5, n = 6 for GPT3.5 and n = 6 for GPT4). In cases of small sample size ( n ≤ 10 for GPT3.5 and GPT4), we plotted the individual results as overlaid dots. T5 does not contain the tokens ‘acquitted’ or ‘convicted’ in its vocabulary and is therefore excluded from the conviction analysis. Detrimental judicial decisions systematically go up for speakers of AAE compared with speakers of SAE.

We then examined decisions about criminality. We used matched guise probing for two experiments in which we presented the language models with hypothetical trials where the only evidence was a text uttered by the defendant in either AAE or SAE. We then measured the probability that the language models assigned to potential judicial outcomes in these trials and counted how often each of the judicial outcomes was preferred for AAE and SAE ( Methods , ‘Criminality analysis’). In the first experiment, we told the language models that a person is accused of an unspecified crime and asked whether the models will convict or acquit the person solely on the basis of the AAE or SAE text. Overall, we found that the rate of convictions was greater for AAE ( r = 68.7%) than SAE ( r = 62.1%; Fig. 3c , left). A chi-squared test found a strong effect ( χ 2 (1, N = 96) = 184.7, P < 0.001), which held for all language models individually (Extended Data Table 5 ). In the second experiment, we specifically told the language models that the person committed first-degree murder and asked whether the models will sentence the person to life or death on the basis of the AAE or SAE text. The overall rate of death sentences was greater for AAE ( r = 27.7%) than for SAE ( r = 22.8%; Fig. 3c , right). A chi-squared test found a strong effect ( χ 2 (1, N = 144) = 425.4, P < 0.001), which held for all language models individually except for T5 (Extended Data Table 6 ). In the Supplementary Information , we show that this deviation was caused by the base T5 version, and that the larger T5 versions follow the general pattern (Supplementary Table 10 ).

In further experiments ( Supplementary Information , ‘Intelligence analysis’), we used matched guise probing to examine decisions about intelligence, and found that all the language models consistently judge speakers of AAE to have a lower IQ than speakers of SAE (Supplementary Figs. 14 and 15 and Supplementary Tables 17 – 19 ).

Resolvability of dialect prejudice

We wanted to know whether the dialect prejudice we observed is resolved by current practices of bias mitigation, such as increasing the size of the language model or including HF in training. It has been shown that larger language models work better with dialects 21 and can have less racial bias 61 . Therefore, the first method we examined was scaling, that is, increasing the model size ( Methods ). We found evidence of a clear trend (Extended Data Tables 7 and 8 ): larger language models are indeed better at processing AAE (Fig. 4a , left), but they are not less prejudiced against speakers of it. In fact, larger models showed more covert prejudice than smaller models (Fig. 4a , right). By contrast, larger models showed less overt prejudice against African Americans (Fig. 4a , right). Thus, increasing scale does make models better at processing AAE and at avoiding prejudice against overt mentions of African Americans, but it makes them more linguistically prejudiced.

a , Language modelling perplexity and stereotype strength on AAE text as a function of model size. Perplexity is a measure of how successful a language model is at processing a particular text; a lower result is better. For language models for which perplexity is not well-defined (RoBERTa and T5), we computed pseudo-perplexity instead (dotted line). Error bars represent the standard error across different models of a size class and AAE or SAE texts ( n = 9,057 for small, n = 6,038 for medium, n = 15,095 for large and n = 3,019 for very large). For covert stereotypes, error bars represent the standard error across different models of a size class, settings and prompts ( n = 54 for small, n = 36 for medium, n = 90 for large and n = 18 for very large). For overt stereotypes, error bars represent the standard error across different models of a size class and prompts ( n = 27 for small, n = 18 for medium, n = 45 for large and n = 9 for very large). Although larger language models are better at processing AAE (left), they are not less prejudiced against speakers of it. Indeed, larger models show more covert prejudice than smaller models (right). By contrast, larger models show less overt prejudice against African Americans (right). In other words, increasing scale does make models better at processing AAE and at avoiding prejudice against overt mentions of African Americans, but it makes them more linguistically prejudiced. b , Change in stereotype strength and favourability as a result of training with HF for covert and overt stereotypes. Error bars represent the standard error across different prompts ( n = 9). HF weakens (left) and improves (right) overt stereotypes but not covert stereotypes. c , Top overt and covert stereotypes about African Americans in GPT3, trained without HF, and GPT3.5, trained with HF. Colour coding as positive (green) and negative (red) is based on ref. 34 . The overt stereotypes get substantially more positive as a result of HF training in GPT3.5, but there is no visible change in favourability for the covert stereotypes.

As a second potential way to resolve dialect prejudice in language models, we examined training with HF 49 , 62 . Specifically, we compared GPT3.5 (ref. 49 ) with GPT3 (ref. 63 ), its predecessor that was trained without using HF ( Methods ). Looking at the top adjectives associated overtly and covertly with African Americans by the two language models, we found that HF resulted in more-positive overt associations but had no clear qualitative effect on the covert associations (Fig. 4c ). This observation was confirmed by quantitative analyses: the inclusion of HF resulted in significantly weaker (no HF, m = 0.135, s = 0.142; HF, m = −0.119, s = 0.234; t (16) = 2.6, P < 0.05) and more favourable (no HF, m = 0.221, s = 0.399; HF, m = 1.047, s = 0.387; t (16) = −6.4, P < 0.001) overt stereotypes but produced no significant difference in the strength (no HF, m = 0.153, s = 0.049; HF, m = 0.187, s = 0.066; t (16) = −1.2, P = 0.3) or unfavourability (no HF, m = −1.146, s = 0.580; HF, m = −1.029, s = 0.196; t (16) = −0.5, P = 0.6) of covert stereotypes (Fig. 4b ). Thus, HF training weakens and ameliorates the overt stereotypes but has no clear effect on the covert stereotypes; in other words, it obscures the racist attitudes on the surface, but more subtle forms of racism, such as dialect prejudice, remain unaffected. This finding is underscored by the fact that the discrepancy between overt and covert stereotypes about African Americans is most pronounced for the two examined language models trained with human feedback (GPT3.5 and GPT4; see ‘Covert stereotypes in language models’). Furthermore, this finding again shows that there is a fundamental difference between overt and covert stereotypes in language models, and that mitigating the overt stereotypes does not automatically translate to mitigated covert stereotypes.

To sum up, neither scaling nor training with HF as applied today resolves the dialect prejudice. The fact that these two methods effectively mitigate racial performance disparities and overt racial stereotypes in language models indicates that this form of covert racism constitutes a different problem that is not addressed by current approaches for improving and aligning language models.

The key finding of this article is that language models maintain a form of covert racial prejudice against African Americans that is triggered by dialect features alone. In our experiments, we avoided overt mentions of race but drew from the racialized meanings of a stigmatized dialect, and could still find historically racist associations with African Americans. The implicit nature of this prejudice, that is, the fact it is about something that is not explicitly expressed in the text, makes it fundamentally different from the overt racial prejudice that has been the focus of previous research. Strikingly, the language models’ covert and overt racial prejudices are often in contradiction with each other, especially for the most recent language models that have been trained with HF (GPT3.5 and GPT4). These two language models obscure the racism, overtly associating African Americans with exclusively positive attributes (such as ‘brilliant’), but our results show that they covertly associate African Americans with exclusively negative attributes (such as ‘lazy’).

We argue that this paradoxical relation between the language models’ covert and overt racial prejudices manifests the inconsistent racial attitudes present in the contemporary society of the United States 8 , 64 . In the Jim Crow era, stereotypes about African Americans were overtly racist, but the normative climate after the civil rights movement made expressing explicitly racist views distasteful. As a result, racism acquired a covert character and continued to exist on a more subtle level. Thus, most white people nowadays report positive attitudes towards African Americans in surveys but perpetuate racial inequalities through their unconscious behaviour, such as their residential choices 65 . It has been shown that negative stereotypes persist, even if they are superficially rejected 66 , 67 . This ambivalence is reflected by the language models we analysed, which are overtly non-racist but covertly exhibit archaic stereotypes about African Americans, showing that they reproduce a colour-blind racist ideology. Crucially, the civil rights movement is generally seen as the period during which racism shifted from overt to covert 68 , 69 , and this is mirrored by our results: all the language models overtly agree the most with human stereotypes from after the civil rights movement, but covertly agree the most with human stereotypes from before the civil rights movement.

Our findings beg the question of how dialect prejudice got into the language models. Language models are pretrained on web-scraped corpora such as WebText 46 , C4 (ref. 48 ) and the Pile 70 , which encode raciolinguistic stereotypes about AAE. A drastic example of this is the use of ‘mock ebonics’ to parodize speakers of AAE 71 . Crucially, a growing body of evidence indicates that language models pick up prejudices present in the pretraining corpus 72 , 73 , 74 , 75 , which would explain how they become prejudiced against speakers of AAE, and why they show varying levels of dialect prejudice as a function of the pretraining corpus. However, the web also abounds with overt racism against African Americans 76 , 77 , so we wondered why the language models exhibit much less overt than covert racial prejudice. We argue that the reason for this is that the existence of overt racism is generally known to people 32 , which is not the case for covert racism 69 . Crucially, this also holds for the field of AI. The typical pipeline of training language models includes steps such as data filtering 48 and, more recently, HF training 62 that remove overt racial prejudice. As a result, much of the overt racism on the web does not end up in the language models. However, there are currently no measures in place to curtail covert racial prejudice when training language models. For example, common datasets for HF training 62 , 78 do not include examples that would train the language models to treat speakers of AAE and SAE equally. As a result, the covert racism encoded in the training data can make its way into the language models in an unhindered fashion. It is worth mentioning that the lack of awareness of covert racism also manifests during evaluation, where it is common to test language models for overt racism but not for covert racism 21 , 63 , 79 , 80 .

As well as the representational harms, by which we mean the pernicious representation of AAE speakers, we also found evidence for substantial allocational harms. This refers to the inequitable allocation of resources to AAE speakers 81 (Barocas et al., unpublished observations), and adds to known cases of language technology putting speakers of AAE at a disadvantage by performing worse on AAE 82 , 83 , 84 , 85 , 86 , 87 , 88 , misclassifying AAE as hate speech 81 , 89 , 90 , 91 or treating AAE as incorrect English 83 , 85 , 92 . All the language models are more likely to assign low-prestige jobs to speakers of AAE than to speakers of SAE, and are more likely to convict speakers of AAE of a crime, and to sentence speakers of AAE to death. Although the details of our tasks are constructed, the findings reveal real and urgent concerns because business and jurisdiction are areas for which AI systems involving language models are currently being developed or deployed. As a consequence, the dialect prejudice we uncovered might already be affecting AI decisions today, for example when a language model is used in application-screening systems to process background information, which might include social-media text. Worryingly, we also observe that larger language models and language models trained with HF exhibit stronger covert, but weaker overt, prejudice. Against the backdrop of continually growing language models and the increasingly widespread adoption of HF training, this has two risks: first, that language models, unbeknownst to developers and users, reach ever-increasing levels of covert prejudice; and second, that developers and users mistake ever-decreasing levels of overt prejudice (the only kind of prejudice currently tested for) for a sign that racism in language models has been solved. There is therefore a realistic possibility that the allocational harms caused by dialect prejudice in language models will increase further in the future, perpetuating the racial discrimination experienced by generations of African Americans.

Matched guise probing examines how strongly a language model associates certain tokens, such as personality traits, with AAE compared with SAE. AAE can be viewed as the treatment condition, whereas SAE functions as the control condition. We start by explaining the basic experimental unit of matched guise probing: measuring how a language model associates certain tokens with an individual text in AAE or SAE. Based on this, we introduce two different settings for matched guise probing (meaning-matched and non-meaning-matched), which are both inspired by the matched guise technique used in sociolinguistics 36 , 37 , 93 , 94 and provide complementary views on the attitudes a language model has about a dialect.

The basic experimental unit of matched guise probing is as follows. Let θ be a language model, t be a text in AAE or SAE, and x be a token of interest, typically a personality trait such as ‘intelligent’. We embed the text in a prompt v , for example v ( t ) = ‘a person who says t tends to be’, and compute P ( x ∣ v ( t ); θ ), which is the probability that θ assigns to x after processing v ( t ). We calculate P ( x ∣ v ( t ); θ ) for equally sized sets T a of AAE texts and T s of SAE texts, comparing various tokens from a set X as possible continuations. It has been shown that P ( x ∣ v ( t ); θ ) can be affected by the precise wording of v , so small modifications of v can have an unpredictable effect on the predictions made by the language model 21 , 95 , 96 . To account for this fact, we consider a set V containing several prompts ( Supplementary Information ). For all experiments, we have provided detailed analyses of variation across prompts in the Supplementary Information .

We conducted matched guise probing in two settings. In the first setting, the texts in T a and T s formed pairs expressing the same underlying meaning, that is, the i -th text in T a (for example, ‘I be so happy when I wake up from a bad dream cus they be feelin too real’) matches the i -th text in T s (for example, ‘I am so happy when I wake up from a bad dream because they feel too real’). For this setting, we used the dataset from ref. 87 , which contains 2,019 AAE tweets together with their SAE translations. In the second setting, the texts in T a and T s did not form pairs, so they were independent texts in AAE and SAE. For this setting, we sampled 2,000 AAE and SAE tweets from the dataset in ref. 83 and used tweets strongly aligned with African Americans for AAE and tweets strongly aligned with white people for SAE ( Supplementary Information (‘Analysis of non-meaning-matched texts’), Supplementary Fig. 1 and Supplementary Table 3 ). In the Supplementary Information , we include examples of AAE and SAE texts for both settings (Supplementary Tables 1 and 2 ). Tweets are well suited for matched guise probing because they are a rich source of dialectal variation 97 , 98 , 99 , especially for AAE 100 , 101 , 102 , but matched guise probing can be applied to any type of text. Although we do not consider it here, matched guise probing can in principle also be applied to speech-based models, with the potential advantage that dialectal variation on the phonetic level could be captured more directly, which would make it possible to study dialect prejudice specific to regional variants of AAE 23 . However, note that a great deal of phonetic variation is reflected orthographically in social-media texts 101 .

It is important to analyse both meaning-matched and non-meaning-matched settings because they capture different aspects of the attitudes a language model has about speakers of AAE. Controlling for the underlying meaning makes it possible to uncover differences in the attitudes of the language model that are solely due to grammatical and lexical features of AAE. However, it is known that various properties other than linguistic features correlate with dialect, such as topics 45 , and these might also influence the attitudes of the language model. Sidelining such properties bears the risk of underestimating the harms that dialect prejudice causes for speakers of AAE in the real world. For example, in a scenario in which a language model is used in the context of automated personnel selection to screen applicants’ social-media posts, the texts of two competing applicants typically differ in content and do not come in pairs expressing the same meaning. The relative advantages of using meaning-matched or non-meaning-matched data for matched guise probing are conceptually similar to the relative advantages of using the same or different speakers for the matched guise technique: more control in the former versus more naturalness in the latter setting 93 , 94 . Because the results obtained in both settings were consistent overall for all experiments, we aggregated them in the main article, but we analysed differences in detail in the Supplementary Information .

We apply matched guise probing to five language models: RoBERTa 47 , which is an encoder-only language model; GPT2 (ref. 46 ), GPT3.5 (ref. 49 ) and GPT4 (ref. 50 ), which are decoder-only language models; and T5 (ref. 48 ), which is an encoder–decoder language model. For each language model, we examined one or more model versions: GPT2 (base), GPT2 (medium), GPT2 (large), GPT2 (xl), RoBERTa (base), RoBERTa (large), T5 (small), T5 (base), T5 (large), T5 (3b), GPT3.5 (text-davinci-003) and GPT4 (0613). Where we used several model versions per language model (GPT2, RoBERTa and T5), the model versions all had the same architecture and were trained on the same data but differed in their size. Furthermore, we note that GPT3.5 and GPT4 are the only language models examined in this paper that were trained with HF, specifically reinforcement learning from human feedback 103 . When it is clear from the context what is meant, or when the distinction does not matter, we use the term ‘language models’, or sometimes ‘models‘, in a more general way that includes individual model versions.

Regarding matched guise probing, the exact method for computing P ( x ∣ v ( t ); θ ) varies across language models and is detailed in the Supplementary Information . For GPT4, for which computing P ( x ∣ v ( t ); θ ) for all tokens of interest was often not possible owing to restrictions imposed by the OpenAI application programming interface (API), we used a slightly modified method for some of the experiments, and this is also discussed in the Supplementary Information . Similarly, some of the experiments could not be done for all language models because of model-specific constraints, which we highlight below. We note that there was at most one language model per experiment for which this was the case.

Covert-stereotype analysis

In the covert-stereotype analysis, the tokens x whose probabilities are measured for matched guise probing are trait adjectives from the Princeton Trilogy 29 , 30 , 31 , 34 , such as ‘aggressive’, ‘intelligent’ and ‘quiet’. We provide details about these adjectives in the Supplementary Information . In the Princeton Trilogy, the adjectives are provided to participants in the form of a list, and participants are asked to select from the list the five adjectives that best characterize a given ethnic group, such as African Americans. The studies that we compare in this paper, which are the original Princeton Trilogy studies 29 , 30 , 31 and a more recent reinstallment 34 , all follow this general set-up and observe a gradual improvement of the expressed stereotypes about African Americans over time, but the exact interpretation of this finding is disputed 32 . Here, we used the adjectives from the Princeton Trilogy in the context of matched guise probing.

Specifically, we first computed P ( x ∣ v ( t ); θ ) for all adjectives, for both the AAE texts and the SAE texts. The method for aggregating the probabilities P ( x ∣ v ( t ); θ ) into association scores between an adjective x and AAE varies for the two settings of matched guise probing. Let ${t}_{{\rm{a}}}^{i}$ be the i -th AAE text in T a and ${t}_{{\rm{s}}}^{i}$ be the i -th SAE text in T s . In the meaning-matched setting, in which ${t}_{{\rm{a}}}^{i}$ and ${t}_{{\rm{s}}}^{i}$ express the same meaning, we computed the prompt-level association score for an adjective x as

where n = ∣ T a ∣ = ∣ T s ∣ . Thus, we measure for each pair of AAE and SAE texts the log ratio of the probability assigned to x following the AAE text and the probability assigned to x following the SAE text, and then average the log ratios of the probabilities across all pairs. In the non-meaning-matched setting, we computed the prompt-level association score for an adjective x as

where again n = ∣ T a ∣ = ∣ T s ∣ . In other words, we first compute the average probability assigned to a certain adjective x following all AAE texts and the average probability assigned to x following all SAE texts, and then measure the log ratio of these average probabilities. The interpretation of q ( x ; v , θ ) is identical in both settings; q ( x ; v , θ ) > 0 means that for a certain prompt v , the language model θ associates the adjective x more strongly with AAE than with SAE, and q ( x ; v , θ ) < 0 means that for a certain prompt v , the language model θ associates the adjective x more strongly with SAE than with AAE. In the Supplementary Information (‘Calibration’), we show that q ( x ; v , θ ) is calibrated 104 , meaning that it does not depend on the prior probability that θ assigns to x in a neutral context.

The prompt-level association scores q ( x ; v , θ ) are the basis for further analyses. We start by averaging q ( x ; v , θ ) across model versions, prompts and settings, and this allows us to rank all adjectives according to their overall association with AAE for individual language models (Fig. 2a ). In this and the following adjective analyses, we focus on the five adjectives that exhibit the highest association with AAE, making it possible to consistently compare the language models with the results from the Princeton Trilogy studies, most of which do not report the full ranking of all adjectives. Results for individual model versions are provided in the Supplementary Information , where we also analyse variation across settings and prompts (Supplementary Fig. 2 and Supplementary Table 4 ).

Next, we wanted to measure the agreement between language models and humans through time. To do so, we considered the five adjectives most strongly associated with African Americans for each study and evaluated how highly these adjectives are ranked by the language models. Specifically, let R l = [ x 1 , …, x ∣ X ∣ ] be the adjective ranking generated by a language model and ${R}_{h}^{5}$ = [ x 1 , …, x 5 ] be the ranking of the top five adjectives generated by the human participants in one of the Princeton Trilogy studies. A typical measure to evaluate how highly the adjectives from ${R}_{h}^{5}$ are ranked within R l is average precision, AP 51 . However, AP does not take the internal ranking of the adjectives in ${R}_{h}^{5}$ into account, which is not ideal for our purposes; for example, AP does not distinguish whether the top-ranked adjective for humans is on the first or on the fifth rank for a language model. To remedy this, we computed the mean average precision, MAP, for different subsets of ${R}_{h}^{5}$ ,

where ${R}_{h}^{i}$ denotes the top i adjectives from the human ranking. MAP = 1 if, and only if, the top five adjectives from ${R}_{h}^{5}$ have an exact one-to-one correspondence with the top five adjectives from R l , so, unlike AP, it takes the internal ranking of the adjectives into account. We computed an individual agreement score for each language model and prompt, so we average the q ( x ; v , θ ) association scores for all model versions of a language model (GPT2, for example) and the two settings (meaning-matched and non-meaning-matched) to generate R l . Because the OpenAI API for GPT4 does not give access to the probabilities for all adjectives, we excluded GPT4 from this analysis. Results are presented in Fig. 2b and Extended Data Table 1 . In the Supplementary Information (‘Agreement analysis’), we analyse variation across model versions, settings and prompts (Supplementary Figs. 3 – 5 ).

To analyse the favourability of the stereotypes about African Americans, we drew from crowd-sourced favourability ratings collected previously 34 for the adjectives from the Princeton Trilogy that range between −2 (‘very unfavourable’, meaning very negative) and 2 (‘very favourable’, meaning very positive). For example, the favourability rating of ‘cruel’ is −1.81 and the favourability rating of ‘brilliant’ is 1.86. We computed the average favourability of the top five adjectives, weighting the favourability ratings of individual adjectives by their association scores with AAE and African Americans. More formally, let R 5 = [ x 1 , …, x 5 ] be the ranking of the top five adjectives generated by either a language model or humans. Furthermore, let f ( x ) be the favourability rating of adjective x as reported in ref. 34 , and let q ( x ) be the overall association score of adjective x with AAE or African Americans that is used to generate R 5 . For the Princeton Trilogy studies, q ( x ) is the percentage of participants who have assigned x to African Americans. For language models, q ( x ) is the average value of q ( x ; v , θ ). We then computed the weighted average favourability, F , of the top five adjectives as

As a result of the weighting, the top-ranked adjective contributed more to the average than the second-ranked adjective, and so on. Results are presented in Extended Data Fig. 1 . To check for consistency, we also computed the average favourability of the top five adjectives without weighting, which yields similar results (Supplementary Fig. 6) .

Overt-stereotype analysis

The overt-stereotype analysis closely followed the methodology of the covert-stereotype analysis, with the difference being that instead of providing the language models with AAE and SAE texts, we provided them with overt descriptions of race (specifically, ‘Black’/‘black’ and ‘White’/‘white’). This methodological difference is also reflected by a different set of prompts ( Supplementary Information ). As a result, the experimental set-up is very similar to existing studies on overt racial bias in language models 4 , 7 . All other aspects of the analysis (such as computing adjective association scores) were identical to the analysis for covert stereotypes. This also holds for GPT4, for which we again could not conduct the agreement analysis.

We again present average results for the five language models in the main article. Results broken down for individual model versions are provided in the Supplementary Information , where we also analyse variation across prompts (Supplementary Fig. 8 and Supplementary Table 5 ).

Employability analysis

The general set-up of the employability analysis was identical to the stereotype analyses: we fed text written in either AAE or SAE, embedded in prompts, into the language models and analysed the probabilities that they assigned to different continuation tokens. However, instead of trait adjectives, we considered occupations for X and also used a different set of prompts ( Supplementary Information ). We created a list of occupations, drawing from previously published lists 6 , 76 , 105 , 106 , 107 . We provided details about these occupations in the Supplementary Information . We then computed association scores q ( x ; v , θ ) between individual occupations x and AAE, following the same methodology as for computing adjective association scores, and ranked the occupations according to q ( x ; v , θ ) for the language models. To probe the prestige associated with the occupations, we drew from a dataset of occupational prestige 105 that is based on the 2012 US General Social Survey and measures prestige on a scale from 1 (low prestige) to 9 (high prestige). For GPT4, we could not conduct the parts of the analysis that require scores for all occupations.

We again present average results for the five language models in the main article. Results for individual model versions are provided in the Supplementary Information , where we also analyse variation across settings and prompts (Supplementary Tables 6 – 8 ).

Criminality analysis

The set-up of the criminality analysis is different from the previous experiments in that we did not compute aggregate association scores between certain tokens (such as trait adjectives) and AAE but instead asked the language models to make discrete decisions for each AAE and SAE text. More specifically, we simulated trials in which the language models were prompted to use AAE or SAE texts as evidence to make a judicial decision. We then aggregated the judicial decisions into summary statistics.

We conducted two experiments. In the first experiment, the language models were asked to determine whether a person accused of committing an unspecified crime should be acquitted or convicted. The only evidence provided to the language models was a statement made by the defendant, which was an AAE or SAE text. In the second experiment, the language models were asked to determine whether a person who committed first-degree murder should be sentenced to life or death. Similarly to the first (general conviction) experiment, the only evidence provided to the language models was a statement made by the defendant, which was an AAE or SAE text. Note that the AAE and SAE texts were the same texts as in the other experiments and did not come from a judicial context. Rather than testing how well language models could perform the tasks of predicting acquittal or conviction and life penalty or death penalty (an application of AI that we do not support), we were interested to see to what extent the decisions of the language models, made in the absence of any real evidence, were impacted by dialect. Although providing the language models with extra evidence as well as the AAE and SAE texts would have made the experiments more similar to real trials, it would have confounded the effect that dialect has on its own (the key effect of interest), so we did not consider this alternative set-up here. We focused on convictions and death penalties specifically because these are the two areas of the criminal justice system for which racial disparities have been described in the most robust and indisputable way: African Americans represent about 12% of the adult population of the United States, but they represent 33% of inmates 108 and more than 41% of people on death row 109 .

Methodologically, we used prompts that asked the language models to make a judicial decision ( Supplementary Information ). For a specific text, t , which is in AAE or SAE, we computed p ( x ∣ v ( t ); θ ) for the tokens x that correspond to the judicial outcomes of interest (‘acquitted’ or ‘convicted’, and ‘life’ or ‘death’). T5 does not contain the tokens ‘acquitted’ and ‘convicted’ in its vocabulary, so is was excluded from the conviction analysis. Because the language models might assign different prior probabilities to the outcome tokens, we calibrated them using their probabilities in a neutral context following v , meaning without text t 104 . Whichever outcome had the higher calibrated probability was counted as the decision. We aggregated the detrimental decisions (convictions and death penalties) and compared their rates (percentages) between AAE and SAE texts. An alternative approach would have been to generate the judicial decision by sampling from the language models, which would have allowed us to induce the language models to generate justifications of their decisions. However, this approach has three disadvantages: first, encoder-only language models such as RoBERTa do not lend themselves to text generation; second, it would have been necessary to apply jail-breaking for some of the language models, which can have unpredictable effects, especially in the context of socially sensitive tasks; and third, model-generated justifications are frequently not aligned with actual model behaviours 110 .

We again present average results on the level of language models in the main article. Results for individual model versions are provided in the Supplementary Information , where we also analyse variation across settings and prompts (Supplementary Figs. 9 and 10 and Supplementary Tables 9 – 12 ).

Scaling analysis

In the scaling analysis, we examined whether increasing the model size alleviated the dialect prejudice. Because the content of the covert stereotypes is quite consistent and does not vary substantially between models with different sizes, we instead analysed the strength with which the language models maintain these stereotypes. We split the model versions of all language models into four groups according to their size using the thresholds of 1.5 × 10 8 , 3.5 × 10 8 and 1.0 × 10 10 (Extended Data Table 7 ).

To evaluate the familiarity of the models with AAE, we measured their perplexity on the datasets used for the two evaluation settings 83 , 87 . Perplexity is defined as the exponentiated average negative log-likelihood of a sequence of tokens 111 , with lower values indicating higher familiarity. Perplexity requires the language models to assign probabilities to full sequences of tokens, which is only the case for GPT2 and GPT3.5. For RoBERTa and T5, we resorted to pseudo-perplexity 112 as the measure of familiarity. Results are only comparable across language models with the same familiarity measure. We excluded GPT4 from this analysis because it is not possible to compute perplexity using the OpenAI API.

To evaluate the stereotype strength, we focused on the stereotypes about African Americans reported in ref. 29 , which the language models’ covert stereotypes agree with most strongly. We split the set of adjectives X into two subsets: the set of stereotypical adjectives in ref. 29 , X s , and the set of non-stereotypical adjectives, X n = X \ X s . For each model with a specific size, we then computed the average value of q ( x ; v , θ ) for all adjectives in X s , which we denote as q s ( θ ), and the average value of q ( x ; v , θ ) for all adjectives in X n , which we denote as q n ( θ ). The stereotype strength of a model θ , or more specifically the strength of the stereotypes about African Americans reported in ref. 29 , can then be computed as

A positive value of δ ( θ ) means that the model associates the stereotypical adjectives in X s more strongly with AAE than the non-stereotypical adjectives in X n , whereas a negative value of δ ( θ ) indicates anti-stereotypical associations, meaning that the model associates the non-stereotypical adjectives in X n more strongly with AAE than the stereotypical adjectives in X s . For the overt stereotypes, we used the same split of adjectives into X s and X n because we wanted to directly compare the strength with which models of a certain size endorse the stereotypes overtly as opposed to covertly. All other aspects of the experimental set-up are identical to the main analyses of covert and overt stereotypes.

HF analysis

We compared GPT3.5 (ref. 49 ; text-davinci-003) with GPT3 (ref. 63 ; davinci), its predecessor language model that was trained without HF. Similarly to other studies that compare these two language models 113 , this set-up allowed us to examine the effects of HF training as done for GPT3.5 in isolation. We compared the two language models in terms of favourability and stereotype strength. For favourability, we followed the methodology we used for the overt-stereotype analysis and evaluated the average weighted favourability of the top five adjectives associated with AAE. For stereotype strength, we followed the methodology we used for the scaling analysis and evaluated the average strength of the stereotypes as reported in ref. 29 .

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All the datasets used in this study are publicly available. The dataset released as ref. 87 can be found at https://aclanthology.org/2020.emnlp-main.473/ . The dataset released as ref. 83 can be found at http://slanglab.cs.umass.edu/TwitterAAE/ . The human stereotype scores used for evaluation can be found in the published articles of the Princeton Trilogy studies 29 , 30 , 31 , 34 . The most recent of these articles 34 also contains the human favourability scores for the trait adjectives. The dataset of occupational prestige that we used for the employability analysis can be found in the corresponding paper 105 . The Brown Corpus 114 , which we used for the Supplementary Information (‘Feature analysis’), can be found at http://www.nltk.org/nltk_data/ . The dataset containing the parallel AAE, Appalachian English and Indian English texts 115 , which we used in the Supplementary Information (‘Alternative explanations’), can be found at https://huggingface.co/collections/SALT-NLP/value-nlp-666b60a7f76c14551bda4f52 .

Code availability

Our code is written in Python and draws on the Python packages openai and transformers for language-model probing, as well as numpy, pandas, scipy and statsmodels for data analysis. The feature analysis described in the Supplementary Information also uses the VALUE Python library 88 . Our code is publicly available on GitHub at https://github.com/valentinhofmann/dialect-prejudice .

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Acknowledgements

V.H. was funded by the German Academic Scholarship Foundation. P.R.K. was funded in part by the Open Phil AI Fellowship. This work was also funded by the Hoffman-Yee Research Grants programme and the Stanford Institute for Human-Centered Artificial Intelligence. We thank A. Köksal, D. Hovy, K. Gligorić, M. Harrington, M. Casillas, M. Cheng and P. Röttger for feedback on an earlier version of the article.

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V.H., P.R.K., D.J. and S.K. designed the research. V.H. performed the research and analysed the data. V.H., P.R.K., D.J. and S.K. wrote the paper.

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Extended data figures and tables

Extended data fig. 1 weighted average favourability of top stereotypes about african americans in humans and top overt as well as covert stereotypes about african americans in language models (lms)..

The overt stereotypes are more favourable than the reported human stereotypes, except for GPT2. The covert stereotypes are substantially less favourable than the least favourable reported human stereotypes from 1933. Results without weighting, which are very similar, are provided in Supplementary Fig. 6 .

Extended Data Fig. 2 Prestige of occupations associated with AAE (positive values) versus SAE (negative values), for individual language models.

The shaded areas show 95% confidence bands around the regression lines. The association with AAE versus SAE is negatively correlated with occupational prestige, for all language models. We cannot conduct this analysis with GPT4 since the OpenAI API does not give access to the probabilities for all occupations.

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Hofmann, V., Kalluri, P.R., Jurafsky, D. et al. AI generates covertly racist decisions about people based on their dialect. Nature 633 , 147–154 (2024). https://doi.org/10.1038/s41586-024-07856-5

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