RAG-RLRC-LaySum at BioLaySumm：集成检索增强生成与可读性控制用于生物医学文本的通俗化摘要
RAG-RLRC-LaySum at BioLaySumm: Integrating Retrieval-Augmented Generation and Readability Control for Layman Summarization of Biomedical Texts
AI总结：
本文提出RAG-RLRC-LaySum框架，结合检索增强生成与可读性控制强化学习，提升生物医学文本通俗摘要的准确性与可读性，在PLOS和eLife数据集上显著超越Plain Gemini模型。
摘要：
本文介绍了RAG-RLRC-LaySum框架，旨在通过高级自然语言处理（NLP）技术使复杂的生物医学研究易于被外行理解。我们的检索增强生成（RAG）解决方案通过重排序方法增强，利用多个知识源确保通俗摘要的准确性和相关性。此外，我们的可读性控制强化学习（RLRC）策略提高了可读性，使非专业人士能够理解科学内容。使用公开的PLOS和eLife数据集进行的评估表明，我们的方法超越了Plain Gemini模型，可读性得分提高20%，ROUGE-2相关性得分提高15%，事实准确性提升10%。RAG-RLRC-LaySum框架有效地使科学知识大众化，增强了公众对生物医学发现的参与度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
