Challenge: a recent study shows that accessing medical literature is difficult for laypeople because it is written for specialists and contains medical jargon.
Approach: They propose a two-stage strategy to identify relevant content to be simplified . they first generate reference summaries via sentence matching between the original and simplified abstracts .
Outcome: The proposed approach improves on a seq2seq-based test set on an English medical corpus . it also improves the SARI score by 1.1% .

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Paragraph-level Simplification of Medical Texts (2021.naacl-main)

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Challenge: Existing methods for simplification of medical texts are limited due to jargon and technical content.
Approach: They propose to automate the simplification of medical texts by penalizing decoders for producing "jargon" terms.
Outcome: The proposed method improves on existing heuristics by penalizing the decoder for producing "jargon" terms.
AutoMeTS: The Autocomplete for Medical Text Simplification (2020.coling-main)

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Challenge: Semi-automated text simplification approaches can be used to simplify text faster and at a higher quality.
Approach: They propose to use autocomplete to simplify medical texts using aligned English Wikipedia sentences and pretrained neural language models to analyze the additional context.
Outcome: The proposed model outperforms the best individual model by 2.1% and achieves a word prediction accuracy of 64.52%.
Replace, Paraphrase or Fine-tune? Evaluating Automatic Simplification for Medical Texts in Spanish (2024.lrec-main)

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Challenge: lexicon-based simplification methods can help patients understand medical documents . but they must ensure that the content is transmitted rigorously and not creating wrong information.
Approach: They tested automatic simplification techniques using a Spanish lexicon of technical and laymen terms.
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Readability Controllable Biomedical Document Summarization (2022.findings-emnlp)

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Challenge: Existing controllable summarization systems for biomedical documents have little attention to readability control, leaving users with incompatible summaries .
Approach: They propose a task of readability controllable summarization for biomedical documents to generate summaries that are incompatible with users' levels of expertise.
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Medical Text Simplification: Optimizing for Readability with Unlikelihood Training and Reranked Beam Search Decoding (2023.findings-emnlp)

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Challenge: Text simplification has emerged as an increasingly useful application of AI for bridging the communication gap in specialized fields such as medicine, where the lexicon is often dominated by technical jargon and complex constructs.
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Benchmarking Automated Clinical Language Simplification: Dataset, Algorithm, and Evaluation (2022.coling-1)

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Challenge: Existing studies to translate medical jargon into layperson-understandable language focus on accuracy and readability aspects of clinical language.
Approach: They propose to construct a dataset to support automated clinical language simplification and propose a model that mimics the human annotation procedure.
Outcome: The proposed model matches human annotation procedures and achieves state-of-the-art performance compared with baselines.
MultiMSD: A Corpus for Multilingual Medical Text Simplification from Online Medical References (2025.findings-acl)

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Challenge: Medical texts contain technical terms, and non-experts often cannot use information effectively.
Approach: They propose a method for training medical text simplification models to actively paraphrase medical terms.
Outcome: The proposed method improves the performance of medical text simplification in nine languages.
Multilingual Simplification of Medical Texts (2023.emnlp-main)

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Challenge: Existing work on medical text simplification has focused on monolingual settings . important findings in medicine are typically presented in technical, jargon-laden language . text simulating models can generate viable simplified texts, but there are outstanding challenges .
Approach: They propose a dataset for medical text simplification in four languages . they evaluate fine-tuned and zero-shot models across these languages based on human assessments and analyses .
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Summarizing, Simplifying, and Synthesizing Medical Evidence using GPT-3 (with Varying Success) (2023.acl-short)

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Challenge: Large language models are capable of producing high quality summaries of general domain news articles in few- and zero-shot settings, but it is unclear whether they are similarly capable in more specialized domains such as biomedicine.
Approach: They use GPT-3 to generate single- and multi-document summaries of biomedical articles, given no supervision, using a set of annotations.
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Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures. (2020.findings-emnlp)

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Challenge: Summarization of medical conversations addresses a very real need in medical practice: capturing the most important aspects of a medical encounter.
Approach: They propose a novel approach to medical conversation summarization that leverages the unique and independent local structures created when gathering a patient’s medical history.
Outcome: The proposed model captures most or all of the information in 80% of the medical conversations making it a realistic alternative to costly manual summarization by medical experts.

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