RAG-RLRC-LaySum at BioLaySumm: Integrating Retrieval-Augmented Generation and Readability Control for Layman Summarization of Biomedical Texts
<|reference_start|>RAG-RLRC-LaySum at BioLaySumm: Integrating Retrieval-Augmented Generation and Readability Control for Layman Summarization of Biomedical Texts: This paper introduces the RAG-RLRC-LaySum framework, designed to make complex biomedical research understandable to laymen through advanced Natural Language Processing (NLP) techniques. Our Retrieval Augmented 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.<|reference_end|>
@article{ji2024rag-rlrc-laysum,
    title={RAG-RLRC-LaySum at BioLaySumm: Integrating Retrieval-Augmented
  Generation and Readability Control for Layman Summarization of Biomedical
  Texts},
    author={Yuelyu Ji, Zhuochun Li, Rui Meng, Sonish Sivarajkumar, Yanshan Wang,
  Zeshui Yu, Hui Ji, Yushui Han, Hanyu Zeng and Daqing He},
    journal={arXiv preprint arXiv:2405.13179},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.13179},
    primaryClass={cs.CL}
}