@inproceedings{kuramoto-etal-2026-medical,
title = "Medical Text Rewriting for Non-Experts: A Guideline-Driven {LLM} Approach",
author = "Kuramoto, Mana and
Nagai, Hiroyuki and
Yamada, Keiko and
Ide, Hiroo and
Hayakawa, Masayo and
Nishiyama, Tomohiro and
Wakamiya, Shoko and
Aramaki, Eiji",
editor = "Gupta, Deepak and
Thompson, Paul and
Ananiadou, Sophia and
Demner-Fushman, Dina",
booktitle = "Proceedings of the Third Workshop on Patient-Oriented Language Processing ({CL}4{H}ealth) @ {LREC} 2026",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.cl4health-1.10/",
doi = "10.63317/37xq8q3m7grc",
pages = "107--116",
abstract = "Medical research is highly specialized, making it difficult for patients and general readers to understand recent findings.Traditionally, text simplification, replacing technical terms with more accessible expressions, has been employed. However, this approach alone is limited in addressing a lack of background knowledge and often results in the loss of important information.Therefore, this study defines ``rewriting for non-experts'' as a rewriting process that, in addition to simplification, supplements essential background knowledge such as the significance of the research and reasons it is needed and proposes a method for implementing this process using large language models (LLMs).To verify the effectiveness of the proposed approach, a quantitative evaluation using automatic metrics was conducted. The results showed that the method combining the guidelines for human text creation with few-shot examples of reference texts achieved the highest scores.The expansion of the guidelines is planned as part of future work to enable the rewriting of scientific and technological information in a form that is accessible to a broader audience."
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<abstract>Medical research is highly specialized, making it difficult for patients and general readers to understand recent findings.Traditionally, text simplification, replacing technical terms with more accessible expressions, has been employed. However, this approach alone is limited in addressing a lack of background knowledge and often results in the loss of important information.Therefore, this study defines “rewriting for non-experts” as a rewriting process that, in addition to simplification, supplements essential background knowledge such as the significance of the research and reasons it is needed and proposes a method for implementing this process using large language models (LLMs).To verify the effectiveness of the proposed approach, a quantitative evaluation using automatic metrics was conducted. The results showed that the method combining the guidelines for human text creation with few-shot examples of reference texts achieved the highest scores.The expansion of the guidelines is planned as part of future work to enable the rewriting of scientific and technological information in a form that is accessible to a broader audience.</abstract>
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%0 Conference Proceedings
%T Medical Text Rewriting for Non-Experts: A Guideline-Driven LLM Approach
%A Kuramoto, Mana
%A Nagai, Hiroyuki
%A Yamada, Keiko
%A Ide, Hiroo
%A Hayakawa, Masayo
%A Nishiyama, Tomohiro
%A Wakamiya, Shoko
%A Aramaki, Eiji
%Y Gupta, Deepak
%Y Thompson, Paul
%Y Ananiadou, Sophia
%Y Demner-Fushman, Dina
%S Proceedings of the Third Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F kuramoto-etal-2026-medical
%X Medical research is highly specialized, making it difficult for patients and general readers to understand recent findings.Traditionally, text simplification, replacing technical terms with more accessible expressions, has been employed. However, this approach alone is limited in addressing a lack of background knowledge and often results in the loss of important information.Therefore, this study defines “rewriting for non-experts” as a rewriting process that, in addition to simplification, supplements essential background knowledge such as the significance of the research and reasons it is needed and proposes a method for implementing this process using large language models (LLMs).To verify the effectiveness of the proposed approach, a quantitative evaluation using automatic metrics was conducted. The results showed that the method combining the guidelines for human text creation with few-shot examples of reference texts achieved the highest scores.The expansion of the guidelines is planned as part of future work to enable the rewriting of scientific and technological information in a form that is accessible to a broader audience.
%R 10.63317/37xq8q3m7grc
%U https://aclanthology.org/2026.cl4health-1.10/
%U https://doi.org/10.63317/37xq8q3m7grc
%P 107-116
Markdown (Informal)
[Medical Text Rewriting for Non-Experts: A Guideline-Driven LLM Approach](https://aclanthology.org/2026.cl4health-1.10/) (Kuramoto et al., CL4Health 2026)
ACL
- Mana Kuramoto, Hiroyuki Nagai, Keiko Yamada, Hiroo Ide, Masayo Hayakawa, Tomohiro Nishiyama, Shoko Wakamiya, and Eiji Aramaki. 2026. Medical Text Rewriting for Non-Experts: A Guideline-Driven LLM Approach. In Proceedings of the Third Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC 2026, pages 107–116, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).