| Challenge: | Existing systems for simplification of complex medical terms are limited in the scope of their topics and require massive cost and effort to keep up with the latest research. |
| Approach: | They propose a fine-grained lexical simplification task and dataset to enable more targeted development and evaluation of systems for replacing or explaining complex biomedical terms. |
| Outcome: | The proposed task and dataset pave the way for development and evaluation of systems for replacing or explaining complex biomedical terms. |
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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. |
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Replace, Paraphrase or Fine-tune? Evaluating Automatic Simplification for Medical Texts in Spanish (2024.lrec-main)
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Leonardo Campillos-Llanos, Ana Rosa Terroba, Rocío Bartolomé, Ana Valverde-Mateos, Cristina González, Adrián Capllonch-Carrión, Jonathan Heras
| 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. |
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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. |
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French Biomedical Text Simplification: When Small and Precise Helps (2020.coling-main)
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| Challenge: | Existing studies on text simplification in English use large parallel monolingual corpora in which one complex sentence is paired with one or more simplified versions. |
| Approach: | They use parallel sentences from existing health comparable corpora in French and WikiLarge corpus translated from English to French and a lexicon that associates medical terms with paraphrases. |
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Evaluation Dataset for Japanese Medical Text Simplification (2024.naacl-srw)
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| Challenge: | Existing studies on medical text simplification in English have not been well explored in Japanese because of the lack of a parallel corpus of this domain. |
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RALS: Resources and Baselines for Romanian Automatic Lexical Simplification (2025.emnlp-main)
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| Challenge: | Text simplification is the process of transforming texts into variants that are simpler to understand by larger audiences or easier to process by existing NLP systems. |
| Approach: | They propose a method for ordering simplification suggestions using a pairwise ranking approximation method, arranging candidates from simple to complex based on a separate set of human judgments. |
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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. |
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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. |
| Approach: | They propose a unlikelihood loss that encourages generation of simpler terms and a reranked beam search decoding method that optimizes for simplicity. |
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Multi-Word Lexical Simplification (2020.coling-main)
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| Challenge: | In text simplification, individual words are replaced with their simpler equivalents, but single word substitutions do not cover the full complexity of techniques humans use to approach text simulating. |
| Approach: | They propose a task of multi-word lexical simplification in which a sentence is made easier to understand by replacing its fragment with a simpler alternative. |
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Multilingual Simplification of Medical Texts (2023.emnlp-main)
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Sebastian Joseph, Kathryn Kazanas, Keziah Reina, Vishnesh Ramanathan, Wei Xu, Byron Wallace, Junyi Jessy Li
| 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 . |
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