FG-RAG：利用上下文感知的细粒度图RAG增强查询聚焦摘要
FG-RAG: Enhancing Query-Focused Summarization with Context-Aware Fine-Grained Graph RAG
AI总结：
本文提出FG-RAG框架，通过上下文感知实体扩展和查询级细粒度摘要，解决现有GraphRAG在查询聚焦摘要任务中缺乏查询感知和上下文信息的问题，并在多个指标上优于其他RAG系统。
摘要：
检索增强生成（RAG）通过引入外部知识，使大型语言模型能够提供更精确、更相关的回答。在查询聚焦摘要（QFS）任务中，基于GraphRAG的方法显著提升了生成回答的全面性和多样性。然而，现有的基于GraphRAG的方法主要侧重于粗粒度信息摘要，缺乏对具体查询的感知，且检索到的内容缺少足够的上下文信息，难以生成全面的回答。为解决当前RAG系统的不足，我们提出了上下文感知的细粒度图RAG（FG-RAG），以提升QFS任务的性能。FG-RAG在图检索中采用上下文感知实体扩展来扩大图中检索实体的覆盖范围，从而为检索内容提供充足的上下文信息。此外，FG-RAG利用查询级细粒度摘要，在回答生成过程中融入细粒度细节，增强生成摘要的查询感知能力。我们的评估表明，FG-RAG在处理QFS任务时，在全面性、多样性和赋能性等多个指标上优于其他RAG系统。我们的实现代码可在 https://github.com/BuptWululu/FG-RAG 获取。
英文摘要：
Retrieval-Augmented Generation (RAG) enables large language models to provide more precise and pertinent responses by incorporating external knowledge. In the Query-Focused Summarization (QFS) task, GraphRAG-based approaches have notably enhanced the comprehensiveness and diversity of generated responses. However, existing GraphRAG-based approaches predominantly focus on coarse-grained information summarization without being aware of the specific query, and the retrieved content lacks sufficient contextual information to generate comprehensive responses. To address the deficiencies of current RAG systems, we propose Context-Aware Fine-Grained Graph RAG (FG-RAG) to enhance the performance of the QFS task. FG-RAG employs Context-Aware Entity Expansion in graph retrieval to expand the coverage of retrieved entities in the graph, thus providing enough contextual information for the retrieved content. Furthermore, FG-RAG utilizes Query-Level Fine-Grained Summarization to incorporate fine-grained details during response generation, enhancing query awareness for the generated summarization. Our evaluation demonstrates that FG-RAG outperforms other RAG systems in multiple metrics of comprehensiveness, diversity, and empowerment when handling the QFS task. Our implementation is available at https://github.com/BuptWululu/FG-RAG.
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。
