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    <abstract>Long-context summarization is pivotal for extracting core insights from extensive documents. While Large Language Models (LLMs) show remarkable capabilities, they frequently encounter attention dilution and hallucination with lengthy inputs. Retrieval-Augmented Generation (RAG) partially mitigates this, but conventional RAG relies on shallow similarity retrieval of fragmented chunks, failing to capture high-level thematic structures and long-range dependencies. Although graph-based RAG approaches have emerged to address these structural limitations, existing solutions, such as Graph of Records (GoR), critically suffer from a fundamental flaw: they paradoxically re-introduce hallucinations by constructing graphs based on unreliable, LLM-generated responses. To overcome these challenges, we introduce Hierarchical Graph of Evidence (HiGoE) (Code link https://github.com/tkw123/HiGOE). HiGoE redefines the retrieval process by replacing unreliable chunk-based methods with a filtered proposition–evidence graph, ensuring verifiable fact grounding and substantially reducing hallucination. Moreover, HiGoE leverages Personalized PageRank (PPR) to cluster related nodes into thematic hierarchies, thereby restoring global document structure and effectively mitigating attention dilution. To model complex, multi-level relations beyond mere shallow similarity, we develop an Enhanced Graph Attention Network. Experiments show HiGoE consistently surpasses baselines in quality and efficiency.</abstract>
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%0 Conference Proceedings
%T HiGoE: Hierarchical Graph of Evidence to Enhance Retrieval-Augmented Generation for Long-context Summarization
%A Yuan, Long
%A Tian, Kaiwen
%A Chen, Zi
%A Zheng, Bolong
%A Ma, Chuan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F yuan-etal-2026-higoe
%X Long-context summarization is pivotal for extracting core insights from extensive documents. While Large Language Models (LLMs) show remarkable capabilities, they frequently encounter attention dilution and hallucination with lengthy inputs. Retrieval-Augmented Generation (RAG) partially mitigates this, but conventional RAG relies on shallow similarity retrieval of fragmented chunks, failing to capture high-level thematic structures and long-range dependencies. Although graph-based RAG approaches have emerged to address these structural limitations, existing solutions, such as Graph of Records (GoR), critically suffer from a fundamental flaw: they paradoxically re-introduce hallucinations by constructing graphs based on unreliable, LLM-generated responses. To overcome these challenges, we introduce Hierarchical Graph of Evidence (HiGoE) (Code link https://github.com/tkw123/HiGOE). HiGoE redefines the retrieval process by replacing unreliable chunk-based methods with a filtered proposition–evidence graph, ensuring verifiable fact grounding and substantially reducing hallucination. Moreover, HiGoE leverages Personalized PageRank (PPR) to cluster related nodes into thematic hierarchies, thereby restoring global document structure and effectively mitigating attention dilution. To model complex, multi-level relations beyond mere shallow similarity, we develop an Enhanced Graph Attention Network. Experiments show HiGoE consistently surpasses baselines in quality and efficiency.
%U https://aclanthology.org/2026.acl-long.902/
%P 19703-19724

Markdown (Informal)

[HiGoE: Hierarchical Graph of Evidence to Enhance Retrieval-Augmented Generation for Long-context Summarization](https://aclanthology.org/2026.acl-long.902/) (Yuan et al., ACL 2026)

• HiGoE: Hierarchical Graph of Evidence to Enhance Retrieval-Augmented Generation for Long-context Summarization (Yuan et al., ACL 2026)

ACL

• Long Yuan, Kaiwen Tian, Zi Chen, Bolong Zheng, and Chuan Ma. 2026. HiGoE: Hierarchical Graph of Evidence to Enhance Retrieval-Augmented Generation for Long-context Summarization. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 19703–19724, San Diego, California, United States. Association for Computational Linguistics.