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    <abstract>Though online medical literature has made health information more available than ever, the barrier of complex medical jargon prevents the general public from understanding it. Though parallel and comparable corpora for Biomedical Text Simplification have been introduced, these conflate the many syntactic and lexical operations involved in simplification. To enable more targeted development and evaluation, we present a fine-grained lexical simplification task and dataset, Jargon Explanations for Biomedical Simplification (JEBS). The JEBS task involves identifying complex terms, classifying how to replace them, and generating replacement text. The JEBS dataset contains 21,595 replacements for 10,314 terms across 400 biomedical abstracts and their manually simplified versions. Additionally, we provide baseline results for a variety of rule-based and transformer-based systems for the three subtasks. The JEBS task, data, and baseline results pave the way for development and rigorous evaluation of systems for replacing or explaining complex biomedical terms.</abstract>
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%0 Conference Proceedings
%T JEBS: A Fine-grained Biomedical Lexical Simplification Task
%A Xia, William
%A Unde, Ishita
%A Ondov, Brian David
%A Demner-Fushman, Dina
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F xia-etal-2025-jebs
%X Though online medical literature has made health information more available than ever, the barrier of complex medical jargon prevents the general public from understanding it. Though parallel and comparable corpora for Biomedical Text Simplification have been introduced, these conflate the many syntactic and lexical operations involved in simplification. To enable more targeted development and evaluation, we present a fine-grained lexical simplification task and dataset, Jargon Explanations for Biomedical Simplification (JEBS). The JEBS task involves identifying complex terms, classifying how to replace them, and generating replacement text. The JEBS dataset contains 21,595 replacements for 10,314 terms across 400 biomedical abstracts and their manually simplified versions. Additionally, we provide baseline results for a variety of rule-based and transformer-based systems for the three subtasks. The JEBS task, data, and baseline results pave the way for development and rigorous evaluation of systems for replacing or explaining complex biomedical terms.
%R 10.18653/v1/2025.findings-acl.907
%U https://aclanthology.org/2025.findings-acl.907/
%U https://doi.org/10.18653/v1/2025.findings-acl.907
%P 17654-17666

Markdown (Informal)

[JEBS: A Fine-grained Biomedical Lexical Simplification Task](https://aclanthology.org/2025.findings-acl.907/) (Xia et al., Findings 2025)

• JEBS: A Fine-grained Biomedical Lexical Simplification Task (Xia et al., Findings 2025)

ACL

• William Xia, Ishita Unde, Brian David Ondov, and Dina Demner-Fushman. 2025. JEBS: A Fine-grained Biomedical Lexical Simplification Task. In Findings of the Association for Computational Linguistics: ACL 2025, pages 17654–17666, Vienna, Austria. Association for Computational Linguistics.