@inproceedings{ji-etal-2024-rag,
title = "{RAG}-{RLRC}-{L}ay{S}um at {B}io{L}ay{S}umm: Integrating Retrieval-Augmented Generation and Readability Control for Layman Summarization of Biomedical Texts",
author = "Ji, Yuelyu and
Li, Zhuochun and
Meng, Rui and
Sivarajkumar, Sonish and
Wang, Yanshan and
Yu, Zeshui and
Ji, Hui and
Han, Yushui and
Zeng, Hanyu and
He, Daqing",
editor = "Demner-Fushman, Dina and
Ananiadou, Sophia and
Miwa, Makoto and
Roberts, Kirk and
Tsujii, Junichi",
booktitle = "Proceedings of the 23rd Workshop on Biomedical Natural Language Processing",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.bionlp-1.75",
pages = "810--817",
abstract = "This paper introduces the RAG-RLRC-LaySum framework, designed to make complex biomedical research accessible to laymen through advanced Natural Language Processing (NLP) techniques. Our innovative Retrieval Augmentation Generation (RAG) solution, enhanced by a reranking method, utilizes multiple knowledge sources to ensure the precision and pertinence of lay summaries. Additionally, our Reinforcement Learning for Readability Control (RLRC) strategy improves readability, making scientific content comprehensible to non-specialists. Evaluations using the publicly accessible PLOS and eLife datasets show that our methods surpass Plain Gemini model, demonstrating a 20{\%} increase in readability scores, a 15{\%} improvement in ROUGE-2 relevance scores, and a 10{\%} enhancement in factual accuracy. The RAG-RLRC-LaySum framework effectively democratizes scientific knowledge, enhancing public engagement with biomedical discoveries.",
}
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%0 Conference Proceedings
%T RAG-RLRC-LaySum at BioLaySumm: Integrating Retrieval-Augmented Generation and Readability Control for Layman Summarization of Biomedical Texts
%A Ji, Yuelyu
%A Li, Zhuochun
%A Meng, Rui
%A Sivarajkumar, Sonish
%A Wang, Yanshan
%A Yu, Zeshui
%A Ji, Hui
%A Han, Yushui
%A Zeng, Hanyu
%A He, Daqing
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Miwa, Makoto
%Y Roberts, Kirk
%Y Tsujii, Junichi
%S Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F ji-etal-2024-rag
%X This paper introduces the RAG-RLRC-LaySum framework, designed to make complex biomedical research accessible to laymen through advanced Natural Language Processing (NLP) techniques. Our innovative Retrieval Augmentation Generation (RAG) solution, enhanced by a reranking method, utilizes multiple knowledge sources to ensure the precision and pertinence of lay summaries. Additionally, our Reinforcement Learning for Readability Control (RLRC) strategy improves readability, making scientific content comprehensible to non-specialists. Evaluations using the publicly accessible PLOS and eLife datasets show that our methods surpass Plain Gemini model, demonstrating a 20% increase in readability scores, a 15% improvement in ROUGE-2 relevance scores, and a 10% enhancement in factual accuracy. The RAG-RLRC-LaySum framework effectively democratizes scientific knowledge, enhancing public engagement with biomedical discoveries.
%U https://aclanthology.org/2024.bionlp-1.75
%P 810-817
Markdown (Informal)
[RAG-RLRC-LaySum at BioLaySumm: Integrating Retrieval-Augmented Generation and Readability Control for Layman Summarization of Biomedical Texts](https://aclanthology.org/2024.bionlp-1.75) (Ji et al., BioNLP-WS 2024)
ACL
- Yuelyu Ji, Zhuochun Li, Rui Meng, Sonish Sivarajkumar, Yanshan Wang, Zeshui Yu, Hui Ji, Yushui Han, Hanyu Zeng, and Daqing He. 2024. RAG-RLRC-LaySum at BioLaySumm: Integrating Retrieval-Augmented Generation and Readability Control for Layman Summarization of Biomedical Texts. In Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, pages 810–817, Bangkok, Thailand. Association for Computational Linguistics.