Papers › FG-RAG: Enhancing Query-Focused Summarization with Context-Aware Fine-Grained Graph RAG

FG-RAG: Enhancing Query-Focused Summarization with Context-Aware Fine-Grained Graph RAG

13 Mar 2025arXiv:2504.07103archive 2025-07-28

Yubin Hong, Chaofan Li, Jingyi Zhang, Yingxia Shao

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.

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Diversity Query-focused Summarization RAG Response Generation Retrieval Retrieval-augmented Generation

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AWARE Adam Attention Attention Dropout BART BERT BPE Dense Connections Dropout Focus Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention RAG Residual Connection Softmax Weight Decay WordPiece

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