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 (2024)

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.

## introduction biomedical research encompasses crucial discoveries, ranging from everyday health concerns to significant disease outbreaks. such studies are essential not only for scientists and doctors but also for journalists and the general public. however, the specialized and complex language typical in these studies often renders the content incomprehensible to those without a scientific background @xcite . to address this issue, the development of automated lay summaries have become increasingly important @xcite . this initiative aims to summarize the detailed aspects of biomedical research into summaries that are both comprehensible and devoid of complicated 1 our code and implementation details are available here: https://github.com/joydajunspacecraft/rag-rlrclaysum jargon. although these systems show great potential, doubts about their accuracy are a major obstacle to their widespread usegabriel et al. (2020); @xcite ; @xcite ; @xcite ; @xcite . our framework integrates specific external explanations for complex terms to further enhance content simplification. in response to the concerns about the integrity of summarized information, our framework employs a "knowledge retrieval" approach within the retrieval-augmented generation (rag) framework. this method uses a neural re-ranker to dynamically integrate trustworthy external knowledge sources like wikipedia, ensuring that summaries are simplified, factually accurate, and contextually relevantlewis et al. (2020); @xcite . the architecture of the proposed rag is illustrated in figure 1 . we also introduce a rewardbased approach to overcome the limitations of traditional fine-tuning, which often produces summaries that have high rouge scores but are not actually readable to humans. this method fine-tunes the model by rewarding outputs that align with read2020 ). we enhance the model's capabilities through wikipedia knowledge retrieval during inference. we utilize large language models (llms) such as chatgpt and gemini to further improve readability and enhance textual clarity by modifying prompts. for controlled text generation, readability scores are utilized to guide the model in generating expected outputs. the outputs of these scores are normalized to ensure text consistency and quality across generated texts. ## methodology first, we employ a retrieval-augmented generation (rag) solution that processes entire papers despite limited input capacity. second, we improve summary quality by optimizing readability using relevant background information. the framework is illustrated in figure 2 . ## conclusion and future work the rag-rlrc-laysum framework effectively simplifies complex biomedical texts, enhancing readability and factual accuracy for lay audiences. it surpasses traditional models, offering new insights into the pivotal role of knowledge retrieval and readability optimization in scientific summarization. future work will expand the framework's knowledge sources and refine how knowledge is utilized, potentially broadening its application across various scientific fields. this will further explore the integration of domain-specific knowledge to improve the precision and relevance of summaries.