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

arXiv: https://arxiv.org/abs/2504.07103 · HTML: https://mlsanity.com/paper/2504.07103/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 18 of its 27 references that are in the map.
- First posted on arXiv 13 Mar 2025.

## Placed in

- Retrieval-augmented generation / [Knowledge-graph and structured-knowledge RAG](https://mlsanity.com/rag/knowledge-graph-and-structured-knowledge-rag/)

## Builds on (most cited first, 18 of 18)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://mlsanity.com/paper/2310.11511/) (2023)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [Least-to-Most Prompting Enables Complex Reasoning in Large Language Models](https://mlsanity.com/paper/2205.10625/) (2022)
- [Improving language models by retrieving from trillions of tokens](https://mlsanity.com/paper/2112.04426/) (2021)
- [In-Context Retrieval-Augmented Language Models](https://mlsanity.com/paper/2302.00083/) (2023)
- [Retrieval Augmentation Reduces Hallucination in Conversation](https://mlsanity.com/paper/2104.07567/) (2021)
- [Retrieval-Augmented Generation for AI-Generated Content: A Survey](https://mlsanity.com/paper/2402.19473/) (2024)
- [Time-Aware Language Models as Temporal Knowledge Bases](https://mlsanity.com/paper/2106.15110/) (2021)
- [MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries](https://mlsanity.com/paper/2401.15391/) (2024)
- [RealTime QA: What's the Answer Right Now?](https://mlsanity.com/paper/2207.13332/) (2022)
- [RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation](https://mlsanity.com/paper/2404.00610/) (2024)
- [Reducing hallucination in structured outputs via Retrieval-Augmented Generation](https://mlsanity.com/paper/2404.08189/) (2024)
- [Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation](https://mlsanity.com/paper/2410.20724/) (2024)
- [Enhancing LLM Factual Accuracy with RAG to Counter Hallucinations: A Case Study on Domain-Specific Queries in Private Knowledge-Bases](https://mlsanity.com/paper/2403.10446/) (2024)
- [Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks](https://mlsanity.com/paper/2305.05862/) (2023)
- [Query Focused Abstractive Summarization: Incorporating Query Relevance, Multi-Document Coverage, and Summary Length Constraints into seq2seq Models](https://mlsanity.com/paper/1801.07704/) (2018)

## Built on by (most cited first, 4 of 4)

- [Synergizing RAG and Reasoning: A Systematic Review](https://mlsanity.com/paper/2504.15909/) (2025)
- [TagRAG: Tag-guided Hierarchical Knowledge Graph Retrieval-Augmented Generation](https://mlsanity.com/paper/2601.05254/) (2025)
- [Less is More: Denoising Knowledge Graphs For Retrieval Augmented Generation](https://mlsanity.com/paper/2510.14271/) (2025)
- [Query-Focused Event Summarization: A Dataset and Benchmark](https://mlsanity.com/paper/2607.11166/) (2026)

## Abstract (arXiv)

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.