用检索增强生成技术构建可解释的科研文献摘要流水线
Ask, Retrieve, Summarize: A Modular Pipeline for Scientific Literature Summarization
- 分步式架构：先生成问题再检索相关内容
- 在SurveySum数据集上多项指标优于现有方法
- 适合需要高效整合多篇论文的研究者
科学论文的爆炸式增长使得研究人员难以及时跟进并有效整合知识。本文提出XSum，一种基于检索增强生成（RAG）的模块化多文档摘要（MDS）框架，用于科学领域文献摘要。该框架包含两个核心组件：问题生成模块和编辑模块。问题生成模块动态生成适配输入论文的问题，确保检索到相关且准确的信息；编辑模块将检索内容整合为结构清晰、符合学术规范并带正确引用的摘要。在SurveySum数据集上的评估显示，XSum在CheckEval、G-Eval和Ref-F1等指标上均有显著提升，展现出强大性能。该工作提供了一个透明、可扩展的科学摘要框架，具有广泛的应用潜力。代码已公开于https://github.com/webis-de/scolia25-xsum。
原文摘要 · Abstract (English)
The exponential growth of scientific publications has made it increasingly difficult for researchers to stay updated and synthesize knowledge effectively. This paper presents XSum, a modular pipeline for multi-document summarization (MDS) in the scientific domain using Retrieval-Augmented Generation (RAG). The pipeline includes two core components: a question-generation module and an editor module. The question-generation module dynamically generates questions adapted to the input papers, ensuring the retrieval of relevant and accurate information. The editor module synthesizes the retrieved content into coherent and well-structured summaries that adhere to academic standards for proper citation. Evaluated on the SurveySum dataset, XSum demonstrates strong performance, achieving considerable improvements in metrics such as CheckEval, G-Eval and Ref-F1 compared to existing approaches. This work provides a transparent, adaptable framework for scientific summarization with potential applications in a wide range of domains. Code available at https://github.com/webis-de/scolia25-xsum
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