# Paragraph-level Simplification of Medical Texts

arXiv: https://arxiv.org/abs/2104.05767 · HTML: https://mlsanity.com/paper/2104.05767/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 57 references that are in the map.
- First posted on arXiv 12 Apr 2021.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [Neural Text Generation with Unlikelihood Training](https://mlsanity.com/paper/1908.04319/) (2019)
- [Construction of the Literature Graph in Semantic Scholar](https://mlsanity.com/paper/1805.02262/) (2018)
- [Don't Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training](https://mlsanity.com/paper/1911.03860/) (2019)
- [Sentence Simplification with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.10931/) (2017)
- [EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing](https://mlsanity.com/paper/1906.08104/) (2019)
- [Elaborative Simplification: Content Addition and Explanation Generation in Text Simplification](https://mlsanity.com/paper/2010.10035/) (2020)
- [Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification](https://mlsanity.com/paper/1904.02767/) (2019)
- [Discourse Level Factors for Sentence Deletion in Text Simplification](https://mlsanity.com/paper/1911.10384/) (2019)

## Built on by (most cited first, 30 of 53)

- [Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense](https://mlsanity.com/paper/2303.13408/) (2023)
- [Task-aware Retrieval with Instructions](https://mlsanity.com/paper/2211.09260/) (2022)
- [Benchmarking large language models for biomedical natural language processing applications and recommendations](https://mlsanity.com/paper/2305.16326/) (2023)
- [Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature](https://mlsanity.com/paper/2210.09932/) (2022)
- [ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Model](https://mlsanity.com/paper/2304.03086/) (2023)
- [GEMv2: Multilingual NLG Benchmarking in a Single Line of Code](https://mlsanity.com/paper/2206.11249/) (2022)
- [Evaluating Factuality in Text Simplification](https://mlsanity.com/paper/2204.07562/) (2022)
- [Generating Summaries with Controllable Readability Levels](https://mlsanity.com/paper/2310.10623/) (2023)
- [Paper Plain: Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processing](https://mlsanity.com/paper/2203.00130/) (2022)
- [Retrieval augmentation of large language models for lay language generation](https://mlsanity.com/paper/2211.03818/) (2022)
- [Readability Controllable Biomedical Document Summarization](https://mlsanity.com/paper/2210.04705/) (2022)
- [README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP](https://mlsanity.com/paper/2312.15561/) (2023)
- [Med-EASi: Finely Annotated Dataset and Models for Controllable Simplification of Medical Texts](https://mlsanity.com/paper/2302.09155/) (2023)
- [Do Text Simplification Systems Preserve Meaning? A Human Evaluation via Reading Comprehension](https://mlsanity.com/paper/2312.10126/) (2023)
- [APPLS: Evaluating Evaluation Metrics for Plain Language Summarization](https://mlsanity.com/paper/2305.14341/) (2023)
- [Elaborative Simplification as Implicit Questions Under Discussion](https://mlsanity.com/paper/2305.10387/) (2023)
- [InfoLossQA: Characterizing and Recovering Information Loss in Text Simplification](https://mlsanity.com/paper/2401.16475/) (2024)
- [NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization](https://mlsanity.com/paper/2302.05574/) (2023)
- [Multilingual Simplification of Medical Texts](https://mlsanity.com/paper/2305.12532/) (2023)
- [SWiPE: A Dataset for Document-Level Simplification of Wikipedia Pages](https://mlsanity.com/paper/2305.19204/) (2023)
- [SciNews: From Scholarly Complexities to Public Narratives -- A Dataset for Scientific News Report Generation](https://mlsanity.com/paper/2403.17768/) (2024)
- [ReadCtrl: Personalizing text generation with readability-controlled instruction learning](https://mlsanity.com/paper/2406.09205/) (2024)
- [Improving Summarization with Human Edits](https://mlsanity.com/paper/2310.05857/) (2023)
- [Clinical Insights: A Comprehensive Review of Language Models in Medicine](https://mlsanity.com/paper/2408.11735/) (2024)
- [FactEHR: A Dataset for Evaluating Factuality in Clinical Notes Using LLMs](https://mlsanity.com/paper/2412.12422/) (2024)
- [Steering Large Language Models with Register Analysis for Arbitrary Style Transfer](https://mlsanity.com/paper/2505.00679/) (2025)
- [Magical: Medical Lay Language Generation via Semantic Invariance and Layperson-tailored Adaptation](https://mlsanity.com/paper/2508.08730/) (2025)
- [Benchmarking GPT-5 for biomedical natural language processing](https://mlsanity.com/paper/2509.04462/) (2025)
- [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)

## Abstract (arXiv)

We consider the problem of learning to simplify medical texts. This is important because most reliable, up-to-date information in biomedicine is dense with jargon and thus practically inaccessible to the lay audience. Furthermore, manual simplification does not scale to the rapidly growing body of biomedical literature, motivating the need for automated approaches. Unfortunately, there are no large-scale resources available for this task. In this work we introduce a new corpus of parallel texts in English comprising technical and lay summaries of all published evidence pertaining to different clinical topics. We then propose a new metric based on likelihood scores from a masked language model pretrained on scientific texts. We show that this automated measure better differentiates between technical and lay summaries than existing heuristics. We introduce and evaluate baseline encoder-decoder Transformer models for simplification and propose a novel augmentation to these in which we explicitly penalize the decoder for producing "jargon" terms; we find that this yields improvements over baselines in terms of readability.