ArticleProceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting2021
Paragraph-level Simplification of Medical Texts.
Article in Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
27 citing papers in PubMed.
- Lessons from the TREC Plain Language Adaptation of Biomedical Abstracts (PLABA) track.Journal of biomedical informatics · 2026Article
- Auditing frontier general-purpose large language models in biomedical tasks: reasoning gains, extraction limits, and benchmark reliability.Research square · 2026Article
- PatientEase-Domain-Aware RAG for Rehabilitation Instruction Simplification.Bioengineering (Basel, Switzerland) · 2025Article
- Article
- Caught in the Web of Words: Do LLMs Fall for Spin in Medical Literature?Proceedings of machine learning research · 2025Article
- Clinical insights: A comprehensive review of language models in medicine.PLOS digital health · 2025Review
- Article
- Benchmarking large language models for biomedical natural language processing applications and recommendations.Nature communications · 2025Article
- Large Language Models in Gastroenterology and Gastrointestinal Surgery: A New Frontier in Patient Communication and Education.Gastroenterology research · 2025Review
- Coherence and comprehensibility: Large language models predict lay understanding of health-related content.Journal of biomedical informatics · 2025Article
- APPLS: Evaluating Evaluation Metrics for Plain Language Summarization.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2024Article
- README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP.Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing · 2024Article
- MedReadMe: A Systematic Study for Fine-grained Sentence Readability in Medical Domain.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2024Article
- Digital health innovation and artificial intelligence in cardiovascular care: a case-based review.NPJ cardiovascular health · 2024Review
- Ascle-A Python Natural Language Processing Toolkit for Medical Text Generation: Development and Evaluation Study.Journal of medical Internet research · 2024Article
- Sentence-Aligned Simplification of Biomedical Abstracts.Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) · 2024Article
- Retrieval augmentation of large language models for lay language generation.Journal of biomedical informatics · 2024Article
- Article
- Faithful AI in Medicine: A Systematic Review with Large Language Models and Beyond.Research square · 2023Article
- The future landscape of large language models in medicine.Communications medicine · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.