Paper Plain: Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processing
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Abstract (arXiv)
When seeking information not covered in patient-friendly documents, like medical pamphlets, healthcare consumers may turn to the research literature. Reading medical papers, however, can be a challenging experience. To improve access to medical papers, we introduce a novel interactive interface-Paper Plain-with four features powered by natural language processing: definitions of unfamiliar terms, in-situ plain language section summaries, a collection of key questions that guide readers to answering passages, and plain language summaries of the answering passages. We evaluate Paper Plain, finding that participants who use Paper Plain have an easier time reading and understanding research papers without a loss in paper comprehension compared to those who use a typical PDF reader. Altogether, the study results suggest that guiding readers to relevant passages and providing plain language summaries, or "gists," alongside the original paper content can make reading medical papers easier and give readers more confidence to approach these papers.
Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Builds on 27 of its 128 references that are in the map. First posted on arXiv 28 Feb 2022.
Builds on
27 of 128 references.
References · 27
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
- 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 895
- 2020SummEval: Re-evaluating Summarization Evaluationcited by 666
- 2019Evaluating the Factual Consistency of Abstractive Text Summarizationcited by 578
- 2021SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarizationcited by 402
- 2021Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metricscited by 285
- 2019ScispaCy: Fast and Robust Models for Biomedical Natural Language Processingcited by 244
- 2020NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraintscited by 146
- 2019A Question-Entailment Approach to Question Answeringcited by 133
- 2017Deceiving Google's Perspective API Built for Detecting Toxic Commentscited by 99
- 2021Sparks: Inspiration for Science Writing using Language Modelscited by 80
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- 2020GO FIGURE: A Meta Evaluation of Factuality in Summarizationcited by 75
- 2020Multi-Fact Correction in Abstractive Text Summarizationcited by 66
- 2021Improving Factual Consistency of Abstractive Summarization via Question Answeringcited by 55
- 2021Paragraph-level Simplification of Medical Textscited by 52
- 2020Automated Lay Language Summarization of Biomedical Scientific Reviewscited by 46
- 2020Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymencited by 45
- 2021Keep it Simple: Unsupervised Simplification of Multi-Paragraph Textcited by 45
- 2020Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and Symbolscited by 36
- 2019Pre-trained Language Model for Biomedical Question Answeringcited by 31
- 2020AutoMeTS: The Autocomplete for Medical Text Simplificationcited by 12
- 2021Hierarchical Summarization for Longform Spoken Dialogcited by 9
- 2021MadDog: A Web-based System for Acronym Identification and Disambiguationcited by 8
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- 2017Interactions between Health Searchers and Search Enginescited by 1
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Citing papers per year: 2023: 7; 2024: 7; 2025: 5; 2026: 4.
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- 2023EvalLM: Interactive Evaluation of Large Language Model Prompts on User-Defined Criteria24 Sep 2023
- 2023The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces25 Mar 2023
- 2023Envisioning the Next-Gen Document Reader15 Feb 2023
- 2023EvalLM: Interactive Evaluation of Large Language Model Prompts on User-Defined Criteriacited by 73
- 2024AI Hallucinations: A Misnomer Worth Clarifyingcited by 34
- 2023Generating Summaries with Controllable Readability Levelscited by 24
- 2023Selenite: Scaffolding Online Sensemaking with Comprehensive Overviews Elicited from Large Language Modelscited by 10
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- 2023The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfacescited by 9
- 2024Steering AI-Driven Personalization of Scientific Text for General Audiencescited by 7
- 2024BLIP: Facilitating the Exploration of Undesirable Consequences of Digital Technologiescited by 6
- 2023Automatic and Human-AI Interactive Text Generationcited by 4
- 2025LLM-based Text Simplification and its Effect on User Comprehension and Cognitive Loadcited by 4
- 2023Medical Text Simplification: Optimizing for Readability with Unlikelihood Training and Reranked Beam Search Decodingcited by 3
- 2024Impact of Large Language Model Assistance on Patients Reading Clinical Notes: A Mixed-Methods Studycited by 3
12 most cited of 21 All 21 (JSON)
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- 2026PaperTok: Exploring the Use of Generative AI for Creating Short-form Videos for Research Communication26 Jan 2026
- 2025Designing Beyond Language: Sociotechnical Barriers in AI Health Technologies for Limited English Proficiency10 Nov 2025
- 2025Mapping Scientific Literature with Large Language Models and Topic Modeling17 Oct 2025
- 2025LLM-based Text Simplification and its Effect on User Comprehension and Cognitive Load4 May 2025
- 2025Words as Bridges: Exploring Computational Support for Cross-Disciplinary Translation Work24 Mar 2025
- 2025Caught in the Web of Words: Do LLMs Fall for Spin in Medical Literature?11 Feb 2025
- 2024Steering AI-Driven Personalization of Scientific Text for General Audiences15 Nov 2024
- 2024DiscipLink: Unfolding Interdisciplinary Information Seeking Process via Human-AI Co-Exploration1 Aug 2024
- 2024BLIP: Facilitating the Exploration of Undesirable Consequences of Digital Technologies10 May 2024
- 2024SciDaSynth: Interactive Structured Data Extraction from Scientific Literature with Large Language Model21 Apr 2024
12 newest of 21 All 21 (JSON)