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

• URL: http://arxiv.org/abs/2504.07103v1
• Date: Thu, 13 Mar 2025 17:42:07 GMT
• Title: FG-RAG: Enhancing Query-Focused Summarization with Context-Aware Fine-Grained Graph RAG
• Authors: Yubin Hong, Chaofan Li, Jingyi Zhang, Yingxia Shao,
• Abstract summary: In the Query-Focused Summarization (QFS) task, GraphRAG-based approaches have notably enhanced the comprehensiveness and diversity of generated responses.<n>Existing GraphRAG-based approaches focus on coarse-grained information summarization without being aware of the specific query.<n>We propose Context-Aware Fine-Grained Graph RAG (FG-RAG) to enhance the performance of the QFS task.
• Score: 12.854423869114292
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• CausalRAG: Integrating Causal Graphs into Retrieval-Augmented Generation [11.265999775635823]
  CausalRAG is a novel framework that incorporates causal graphs into the retrieval process. By constructing and tracing causal relationships, CausalRAG preserves contextual continuity and improves retrieval precision. Our findings suggest that grounding retrieval in causal reasoning provides a promising approach to knowledge-intensive tasks.
  arXiv  Detail & Related papers  (2025-03-25T17:43:08Z)
• RGL: A Graph-Centric, Modular Framework for Efficient Retrieval-Augmented Generation on Graphs [58.10503898336799]
  We introduce the RAG-on-Graphs Library (RGL), a modular framework that seamlessly integrates the complete RAG pipeline.<n>RGL addresses key challenges by supporting a variety of graph formats and integrating optimized implementations for essential components.<n>Our evaluations demonstrate that RGL not only accelerates the prototyping process but also enhances the performance and applicability of graph-based RAG systems.
  arXiv  Detail & Related papers  (2025-03-25T03:21:48Z)
• KG-IRAG: A Knowledge Graph-Based Iterative Retrieval-Augmented Generation Framework for Temporal Reasoning [18.96570718233786]
  GraphRAG has proven highly effective in enhancing the performance of Large Language Models (LLMs) on tasks that require external knowledge.<n>This paper presents Knowledge Graph-Based Iterative Retrieval-Augmented Generation (KG-IRAG), a novel framework that integrates KGs with iterative reasoning.<n>Three new datasets are formed to evaluate KG-IRAG's performance, demonstrating its potential beyond traditional RAG applications.
  arXiv  Detail & Related papers  (2025-03-18T13:11:43Z)
• G-OSR: A Comprehensive Benchmark for Graph Open-Set Recognition [54.45837774534411]
  We introduce textbfG-OSR, a benchmark for evaluating Graph Open-Set Recognition (GOSR) methods at both the node and graph levels. Results offer critical insights into the generalizability and limitations of current GOSR methods.
  arXiv  Detail & Related papers  (2025-03-01T13:02:47Z)
• RAG vs. GraphRAG: A Systematic Evaluation and Key Insights [42.31801859160484]
  We systematically evaluate Retrieval-Augmented Generation (RAG) and GraphRAG on text-based benchmarks.<n>Our results highlight the distinct strengths of RAG and GraphRAG across different tasks and evaluation perspectives.
  arXiv  Detail & Related papers  (2025-02-17T02:36:30Z)
• GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation [84.41557981816077]
  We introduce GFM-RAG, a novel graph foundation model (GFM) for retrieval augmented generation.<n>GFM-RAG is powered by an innovative graph neural network that reasons over graph structure to capture complex query-knowledge relationships.<n>It achieves state-of-the-art performance while maintaining efficiency and alignment with neural scaling laws.
  arXiv  Detail & Related papers  (2025-02-03T07:04:29Z)
• Retrieval-Augmented Generation with Graphs (GraphRAG) [84.29507404866257]
  Retrieval-augmented generation (RAG) is a powerful technique that enhances downstream task execution by retrieving additional information.<n>Graph, by its intrinsic "nodes connected by edges" nature, encodes massive heterogeneous and relational information.<n>Unlike conventional RAG, the uniqueness of graph-structured data, such as diverse-formatted and domain-specific relational knowledge, poses unique and significant challenges when designing GraphRAG for different domains.
  arXiv  Detail & Related papers  (2024-12-31T06:59:35Z)
• Graph Retrieval-Augmented Generation: A Survey [28.979898837538958]
  Retrieval-Augmented Generation (RAG) has achieved remarkable success in addressing the challenges of Large Language Models (LLMs) without necessitating retraining. This paper provides the first comprehensive overview of GraphRAG methodologies. We formalize the GraphRAG workflow, encompassing Graph-Based Indexing, Graph-Guided Retrieval, and Graph-Enhanced Generation.
  arXiv  Detail & Related papers  (2024-08-15T12:20:24Z)
• From Local to Global: A Graph RAG Approach to Query-Focused Summarization [4.075260785658849]
  GraphRAG is a graph-based approach to question answering over private text corpora.<n>We show that GraphRAG leads to substantial improvements over a conventional RAG baseline for both the comprehensiveness and diversity of generated answers.
  arXiv  Detail & Related papers  (2024-04-24T18:38:11Z)
• CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models [49.16989035566899]
  Retrieval-Augmented Generation (RAG) is a technique that enhances the capabilities of large language models (LLMs) by incorporating external knowledge sources. This paper constructs a large-scale and more comprehensive benchmark, and evaluates all the components of RAG systems in various RAG application scenarios.
  arXiv  Detail & Related papers  (2024-01-30T14:25:32Z)
• Generation-Augmented Retrieval for Open-domain Question Answering [134.27768711201202]
  Generation-Augmented Retrieval (GAR) for answering open-domain questions. We show that generating diverse contexts for a query is beneficial as fusing their results consistently yields better retrieval accuracy. GAR achieves state-of-the-art performance on Natural Questions and TriviaQA datasets under the extractive QA setup when equipped with an extractive reader.
  arXiv  Detail & Related papers  (2020-09-17T23:08:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.