# Paper Plain: Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processing

arXiv: https://arxiv.org/abs/2203.00130 · HTML: https://mlsanity.com/paper/2203.00130/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 128 references that are in the map.
- First posted on arXiv 28 Feb 2022.

## Builds on (most cited first, 27 of 27)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [On Faithfulness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2005.00661/) (2020)
- [SummEval: Re-evaluating Summarization Evaluation](https://mlsanity.com/paper/2007.12626/) (2020)
- [Evaluating the Factual Consistency of Abstractive Text Summarization](https://mlsanity.com/paper/1910.12840/) (2019)
- [SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization](https://mlsanity.com/paper/2111.09525/) (2021)
- [Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics](https://mlsanity.com/paper/2104.13346/) (2021)
- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)
- [NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints](https://mlsanity.com/paper/2010.12884/) (2020)
- [A Question-Entailment Approach to Question Answering](https://mlsanity.com/paper/1901.08079/) (2019)
- [Deceiving Google's Perspective API Built for Detecting Toxic Comments](https://mlsanity.com/paper/1702.08138/) (2017)
- [Sparks: Inspiration for Science Writing using Language Models](https://mlsanity.com/paper/2110.07640/) (2021)
- [GO FIGURE: A Meta Evaluation of Factuality in Summarization](https://mlsanity.com/paper/2010.12834/) (2020)
- [Multi-Fact Correction in Abstractive Text Summarization](https://mlsanity.com/paper/2010.02443/) (2020)
- [Improving Factual Consistency of Abstractive Summarization via Question Answering](https://mlsanity.com/paper/2105.04623/) (2021)
- [Paragraph-level Simplification of Medical Texts](https://mlsanity.com/paper/2104.05767/) (2021)
- [Automated Lay Language Summarization of Biomedical Scientific Reviews](https://mlsanity.com/paper/2012.12573/) (2020)
- [Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen](https://mlsanity.com/paper/2005.00701/) (2020)
- [Keep it Simple: Unsupervised Simplification of Multi-Paragraph Text](https://mlsanity.com/paper/2107.03444/) (2021)
- [Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and Symbols](https://mlsanity.com/paper/2009.14237/) (2020)
- [Pre-trained Language Model for Biomedical Question Answering](https://mlsanity.com/paper/1909.08229/) (2019)
- [AutoMeTS: The Autocomplete for Medical Text Simplification](https://mlsanity.com/paper/2010.10573/) (2020)
- [Hierarchical Summarization for Longform Spoken Dialog](https://mlsanity.com/paper/2108.09597/) (2021)
- [MadDog: A Web-based System for Acronym Identification and Disambiguation](https://mlsanity.com/paper/2101.09893/) (2021)
- [Talk to Papers: Bringing Neural Question Answering to Academic Search](https://mlsanity.com/paper/2004.02002/) (2020)
- [Hone as You Read: A Practical Type of Interactive Summarization](https://mlsanity.com/paper/2105.02923/) (2021)
- [Interactions between Health Searchers and Search Engines](https://mlsanity.com/paper/1712.03622/) (2017)

## Built on by (most cited first, 23 of 23)

- [EvalLM: Interactive Evaluation of Large Language Model Prompts on User-Defined Criteria](https://mlsanity.com/paper/2309.13633/) (2023)
- [AI Hallucinations: A Misnomer Worth Clarifying](https://mlsanity.com/paper/2401.06796/) (2024)
- [Generating Summaries with Controllable Readability Levels](https://mlsanity.com/paper/2310.10623/) (2023)
- [Selenite: Scaffolding Online Sensemaking with Comprehensive Overviews Elicited from Large Language Models](https://mlsanity.com/paper/2310.02161/) (2023)
- [DiscipLink: Unfolding Interdisciplinary Information Seeking Process via Human-AI Co-Exploration](https://mlsanity.com/paper/2408.00447/) (2024)
- [The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces](https://mlsanity.com/paper/2303.14334/) (2023)
- [Steering AI-Driven Personalization of Scientific Text for General Audiences](https://mlsanity.com/paper/2411.09969/) (2024)
- [BLIP: Facilitating the Exploration of Undesirable Consequences of Digital Technologies](https://mlsanity.com/paper/2405.06783/) (2024)
- [Automatic and Human-AI Interactive Text Generation](https://mlsanity.com/paper/2310.03878/) (2023)
- [LLM-based Text Simplification and its Effect on User Comprehension and Cognitive Load](https://mlsanity.com/paper/2505.01980/) (2025)
- [Medical Text Simplification: Optimizing for Readability with Unlikelihood Training and Reranked Beam Search Decoding](https://mlsanity.com/paper/2310.11191/) (2023)
- [Impact of Large Language Model Assistance on Patients Reading Clinical Notes: A Mixed-Methods Study](https://mlsanity.com/paper/2401.09637/) (2024)
- [SciDaSynth: Interactive Structured Data Extraction from Scientific Literature with Large Language Model](https://mlsanity.com/paper/2404.13765/) (2024)
- [Image First or Text First? Optimising the Sequencing of Modalities in Large Language Model Prompting and Reasoning Tasks](https://mlsanity.com/paper/2410.03062/) (2024)
- [Caught in the Web of Words: Do LLMs Fall for Spin in Medical Literature?](https://mlsanity.com/paper/2502.07963/) (2025)
- [Words as Bridges: Exploring Computational Support for Cross-Disciplinary Translation Work](https://mlsanity.com/paper/2503.18471/) (2025)
- [Designing Beyond Language: Sociotechnical Barriers in AI Health Technologies for Limited English Proficiency](https://mlsanity.com/paper/2511.07277/) (2025)
- [greCAPTCHA: Assessing Understanding as Evidence of Research Authorship Under Generative AI](https://mlsanity.com/paper/2609.20481/) (2026)
- [Envisioning the Next-Gen Document Reader](https://mlsanity.com/paper/2302.07492/) (2023)
- [Mapping Scientific Literature with Large Language Models and Topic Modeling](https://mlsanity.com/paper/2510.16152/) (2025)
- [PaperTok: Exploring the Use of Generative AI for Creating Short-form Videos for Research Communication](https://mlsanity.com/paper/2601.18218/) (2026)
- [QChunker: Learning Question-Aware Text Chunking for Domain RAG via Multi-Agent Debate](https://mlsanity.com/paper/2603.11650/) (2026)
- [ReFinE: Streamlining UI Mockup Iteration with Research Findings](https://mlsanity.com/paper/2604.04353/) (2026)

## 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.