Paragraph-level Simplification of Medical Texts
Open accessAbstract
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.
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Through their text simplification capabilities, LLMs may improve communication between healthcare staff and patients.
“Through their text simplification capabilities33, LLMs may improve communication between healthcare staff and patients34.”
From The future landscape of large language models in medicine (2023) · cites this paper as [33]
Technical options to overcome limitations in factuality and mitigate bias-related harms can generally be implemented throughout the entire development process of LLMs.
“Technical options to overcome limitations in factuality and mitigate (bias-related) harms can generally be implemented throughout the entire development process of LLMs.”
From The future landscape of large language models in medicine (2023) · cites this paper as [33]
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- Authors
- Devaraj, A., Marshall, I., Wallace, B., & Li, J. J.
- Affiliations
- The University of Texas at Austin (US)
- Northeastern University (US)
- Venue
- Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Published
- 2021
- Volume
- 2021
- Pages
- 4972-4984
- PMC
- PMC9161242
- OpenAlex
- W3155887455
- Cited by
- 79
- References
- 53
- Citations by year
- 2026: 10 · 2025: 16 · 2024: 15 · 2023: 25 · 2022: 13
- FWCI
- 6.12
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- Research Portal (King's College London) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1850153)
- University of Texas at Austin
- National Institutes of Health (5r01lm012086-08)
- U.S. National Library of Medicine (R01-LM012086)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Text Readability and Simplification
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- medical texts, jargon, parallel corpus, masked language models, transformer encoder-decoder, readability, biomedical literature
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