---
title: "StrucSum — paper by Yuan et al. (2025) — summary and ideas"
description: "Large language models can choose summary sentences without training, but on long texts they often… 39 references, cited by 8 papers here."
authors: ["Haohan Yuan", "Sukhwa Hong", "Haopeng Zhang"]
year: 2025
venue: "arXiv:2505.22950"
source: "https://arxiv.org/abs/2505.22950"
doi: "10.48550/arxiv.2505.22950"
arxiv: "2505.22950"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/yuan-2025-strucsum-graph-structured-reasoning-long/"
---
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# StrucSum: Graph-Structured Reasoning for Long Document Extractive Summarization with LLMs

**Authors:** [Haohan Yuan](https://md.research.elamur.ai/free/en/authors/haohan-yuan/), Sukhwa Hong and [Haopeng Zhang](https://md.research.elamur.ai/free/en/authors/haopeng-zhang/) · **Year:** 2025 · **Venue:** [arXiv:2505.22950](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2025](https://md.research.elamur.ai/free/en/venues/findings/2025/)) · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2505.22950) · [PDF](https://arxiv.org/pdf/2505.22950) · [DOI 10.48550/arxiv.2505.22950](https://doi.org/10.48550/arxiv.2505.22950) · [arXiv 2505.22950](https://arxiv.org/abs/2505.22950) · [OpenAlex](https://openalex.org/W7140131920)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · [8](https://md.research.elamur.ai/free/en/papers/yuan-2025-strucsum-graph-structured-reasoning-long/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Large language models can choose summary sentences without training, but on long texts they often miss how the document is organized and which content is central. StrucSum targets this problem in zero-shot extractive summarization. It converts each document into a Text-Attributed Graph. Sentences are nodes embedded with Sentence-BERT, and two sentences are linked when their cosine similarity exceeds a threshold θ. Signals from this graph are then written into the prompt in one of three ways:
- **Neighbor-Aware Prompting (NAP)** lists each sentence's graph neighbors.
- **Centrality-Aware Prompting (CAP)** adds a normalized degree centrality score.
- **Centrality-Guided Masking (CGM)** removes weakly connected sentences to shorten the input.

No model weights are changed. The authors tested the method on ArXiv, PubMed and Multi-News with models such as GPT-4o and LLaMA3-70B. It outperformed plain prompting and unsupervised baselines on both reference-overlap and factuality metrics. The study shows that simple, interpretable graph cues can steer black-box LLMs on long documents. It also gives practical advice on which strategy suits which goal.

## Key points
- On ArXiv, FactCC rose by 19.2 points and SummaC by 8.0 points relative to the baselines.
- Each strategy has its own strength. CGM gives the best overlap with reference summaries, NAP preserves factual consistency best, and CAP balances quality against input length. Combining strategies brought no clear additional gain.
- Prompts containing centrality (CAP or combined setups) produced significant positive Spearman correlations (p \< 0.05) between sentence centrality and selection rank. NAP and CGM alone did not.
- In a graph-only test with GPT4o-mini, sentence text was removed from the prompt. Textual neighbor lists did better than numeric or binary adjacency matrices, but still fell short of NAP with full text (ArXiv ROUGE-2: 11.87 vs 14.59). Structure therefore cannot replace content.
- Limitations: evaluation used 200 test documents per dataset and English only, with no comparison to supervised models. Code is available at github.com/HaohanYuan01/StrucSum.

*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

- [University of Hawaiʻi at Mānoa](https://md.research.elamur.ai/free/en/universities/university-of-hawai-i-at-manoa/) — University, Honolulu, United States · 4 works · 20 citations
- [University of Hawaii at Hilo](https://md.research.elamur.ai/free/en/universities/university-of-hawaii-at-hilo/) — University, Hilo, United States · 2 works · 9 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works

🧭 Topics (2): [🧮 Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) · 1,426 works · [📝 Summarization](https://md.research.elamur.ai/free/en/topics/summarization/) · 232 works

## Cite

Cite as: Haohan Yuan, Sukhwa Hong and Haopeng Zhang. 2025. StrucSum: Graph-Structured Reasoning for Long Document Extractive Summarization with LLMs. arXiv:2505.22950.

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

```bibtex
@misc{yuan2025strucsum,
  title = {{StrucSum: Graph-Structured Reasoning for Long Document Extractive Summarization with LLMs}},
  author = {Haohan Yuan, Sukhwa Hong and Haopeng Zhang},
  year = {2025},
  doi = {10.48550/arxiv.2505.22950},
  eprint = {2505.22950},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2505.22950}
}
```

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*Updated 30 October 2026*