---
title: "HiGoE — paper by Yuan et al. (2026) — summary and ideas"
description: "HiGoE instead builds its graph from atomic propositions that an LLM extracts from text chunks. 55 references."
authors: ["Long Yuan", "Kaiwen Tian", "Zi Chen", "Bolong Zheng", "Chuan Ma"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.902/"
doi: "10.18653/v1/2026.acl-long.902"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/yuan-2026-higoe-hierarchical-graph-evidence-enhance/"
---
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# HiGoE: Hierarchical Graph of Evidence to Enhance Retrieval-Augmented Generation for Long-context Summarization

**Authors:** [Long Yuan](https://md.research.elamur.ai/free/en/authors/long-yuan/), Kaiwen Tian, Zi Chen, Bolong Zheng and Chuan Ma · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.902/) · [PDF](https://aclanthology.org/2026.acl-long.902.pdf) · [DOI 10.18653/v1/2026.acl-long.902](https://doi.org/10.18653/v1/2026.acl-long.902) · [OpenAlex](https://openalex.org/W7166863301)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Long documents strain LLMs in two ways: attention spreads too thin, and the models tend to invent content. Standard RAG fetches isolated chunks by surface similarity, which loses the document's overall structure. Graph-based RAG such as GoR builds its index from LLM-generated query–response pairs, so hallucinations can enter the index itself.

HiGoE instead builds its graph from atomic propositions that an LLM extracts from text chunks. A dense retriever (Contriever) links each proposition to supporting passages. Only propositions that pass both a rule-based filter and an LLM-as-a-Judge check are kept. Personalized PageRank (PPR) then groups nodes into thematic communities, and an LLM writes a summary node for each one. This lets retrieval work at both overview and detail levels. An Enhanced GAT, trained self-supervised with a combined contrastive and ranking loss, learns node embeddings aligned with summary queries.

On five English benchmarks, the authors report that HiGoE beats GoR and other baselines in both summary quality and speed. This matters for faithful query-focused summarization of long reports, transcripts and books.

## Key points
- The benchmarks are QMSum, WCEP, BookSum, GovReport and SQuALITY. The exact quality scores fall in a section missing from the extracted text.
- Offline graph construction averages 208.33 s/document for HiGoE versus 269.45 s/document for GoR.
- Online retrieval (excluding LLM generation) averages 0.425 s/query for HiGoE versus 0.468 s/query for GoR, roughly 9% faster. The largest gains are on WCEP (16.8%) and GovReport (12.5%).
- The pipeline has four stages: proposition–evidence graph construction with two-stage filtering, PPR-based hierarchy, self-supervised training signals, and Enhanced GAT encoding. Code is available at github.com/tkw123/HiGOE.
- The authors note limitations: experiments cover only English, and multilingual adaptation (relevant to Ukrainian use) is left as future work.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Nanjing University of Science and Technology](https://md.research.elamur.ai/free/en/universities/nanjing-university-of-science-and-technology/) — University, Nanjing, China · 19 works · 1,590 citations
- [Wuhan University of Technology](https://md.research.elamur.ai/free/en/universities/wuhan-university-of-technology/) — University, Wuhan, China · 6 works · 261 citations
- [Chongqing University](https://md.research.elamur.ai/free/en/universities/chongqing-university/) — University, Chongqing, China · 14 works · 318 citations

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🧭 Topics (2): [📝 Summarization](https://md.research.elamur.ai/free/en/topics/summarization/) · 243 works · [🔍 Information retrieval & search](https://md.research.elamur.ai/free/en/topics/information-retrieval/) · 552 works

## Cite

Cite as: 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. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{yuan2026higoe,
  title = {{HiGoE: Hierarchical Graph of Evidence to Enhance Retrieval-Augmented Generation for Long-context Summarization}},
  author = {Long Yuan, Kaiwen Tian, Zi Chen, Bolong Zheng and Chuan Ma},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.902},
  url = {https://doi.org/10.18653/v1/2026.acl-long.902}
}
```

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- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/36/)

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*Updated 2 November 2026*