---
title: "Graph-based Neural Multi-Document — Yasunaga 2017"
description: "Neural summarizers had worked well for single documents, but in multi-document summarization (MDS) earlier neural systems scored each sentence of a…"
authors: ["Michihiro Yasunaga", "Rui Zhang", "Kshitijh Meelu", "Ayush Pareek", "Krishnan Srinivasan", "Dragomir Radev"]
year: 2017
venue: "Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)"
source: "https://aclanthology.org/K17-1045/"
doi: "10.18653/v1/k17-1045"
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/yasunaga-2017-graph-based-neural-multi-document/"
---
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# Graph-based Neural Multi-Document Summarization

**Authors:** [Michihiro Yasunaga](https://md.research.elamur.ai/free/en/authors/michihiro-yasunaga/), [Rui Zhang](https://md.research.elamur.ai/free/en/authors/rui-zhang/), Kshitijh Meelu, [Ayush Pareek](https://md.research.elamur.ai/free/en/authors/ayush-pareek/), Krishnan Srinivasan, [Dragomir Radev](https://md.research.elamur.ai/free/en/authors/dragomir-radev/) · **Year:** 2017 · **Venue:** Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/K17-1045/) · [PDF](https://aclanthology.org/K17-1045.pdf) · [DOI 10.18653/v1/k17-1045](https://doi.org/10.18653/v1/k17-1045) · [OpenAlex](https://openalex.org/W2964144561)

**Citations:** 262 citations (openalex-snapshot · 2026-10-14) · [84](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Neural summarizers had worked well for single documents, but in multi-document summarization (MDS) earlier neural systems scored each sentence of a cluster in isolation. This ignored how sentences from different documents relate to one another. Yasunaga et al. add explicit sentence relation graphs to a neural extractive pipeline. Each sentence is encoded with a GRU (GRU sent). A Graph Convolutional Network (GCN) then propagates these embeddings over a graph of the whole cluster. A second GRU (GRU doc) builds a cluster embedding, and a regression layer scores salience. Sentences are then picked greedily, skipping redundant ones, until the length limit is reached. The authors compare three graphs: a tf-idf cosine similarity graph, the Approximate Discourse Graph (ADG) from G-Flow, and their new Personalized Discourse Graph (PDG). The PDG reweights ADG edges using sentence-level surface features. On DUC 2004 the model outperforms classic graph-based extractors and a GRU baseline that uses no graph. It is also competitive with state-of-the-art MDS systems. The work shows that graph structure can make up for scarce MDS training data and helps RNNs capture discourse across documents.

## Key points
- Evaluated on the DUC 2004 MDS task. The model splits the work into salience estimation and sentence selection, so it needs no costly sequence-to-sequence decoder.
- The cosine graph connects two sentences when their tf-idf bag-of-words similarity is above 0.2. The ADG counts discourse cues such as co-reference, discourse markers, deverbal nouns and entity or event continuation.
- ADG edge weights grow in steps of 0.5, which makes them coarse and inflates node degree. The PDG multiplies each weight by a linear-regression personalization score built from features such as position, length and proper-noun count, then normalizes it.
- Node degree and salience correlate positively in all three graphs, with strength ordered PDG > ADG > cosine. The PDG also gave the lowest training and validation cost.
- Choosing sentences purely by PDG node degree gave weak summaries, so the graphs are useful mainly as input to the GCN. A case study on cluster d30011t shows that salient sentences have high degree and are linked across documents.

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

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [Discourse analysis](https://md.research.elamur.ai/free/en/topics/discourse-analysis/)

## Cited by

Cited by

84 papers of our library cite it (of the 140673 whose bibliographies we read); 9 of them have a page here, 75 not yet.

- [Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised](https://md.research.elamur.ai/free/en/papers/angelidis-2018-summarizing-opinions-aspect-extraction-meets/)
- [Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model](https://md.research.elamur.ai/free/en/papers/fabbri-2019-multi-news-large-scale-multi/)
- [Extractive Summarization Considering Discourse and Coreference Relations based on Heterogeneous Graph](https://md.research.elamur.ai/free/en/papers/huang-2021-extractive-summarization-considering-discourse-coreference/)
- [Hierarchical Transformers for Multi-Document Summarization](https://md.research.elamur.ai/free/en/papers/liu-2019-hierarchical-transformers-multi-document-summarization/)
- [Coreference-Aware Dialogue Summarization](https://md.research.elamur.ai/free/en/papers/liu-2021-coreference-aware-dialogue-summarization/)
- [Ranking Sentences for Extractive Summarization with Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/narayan-2018-ranking-sentences-extractive-summarization-reinforcement/)
- [PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization](https://md.research.elamur.ai/free/en/papers/xiao-2022-primera-pyramid-based-masked-sentence/)
- [Discourse-Aware Neural Extractive Text Summarization](https://md.research.elamur.ai/free/en/papers/xu-2019-discourse-aware-neural-extractive-text/)
- [LinkBERT: Pretraining Language Models with Document Links](https://md.research.elamur.ai/free/en/papers/yasunaga-2022-linkbert-pretraining-language-models-document/)

## Cite

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

```bibtex
@inproceedings{yasunaga2017graph,
  title = {{Graph-based Neural Multi-Document Summarization}},
  author = {Yasunaga, Michihiro and Zhang, Rui and Meelu, Kshitijh and Pareek, Ayush and Srinivasan, Krishnan and Radev, Dragomir},
  booktitle = {Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)},
  year = {2017},
  doi = {10.18653/v1/k17-1045},
  url = {https://doi.org/10.18653/v1/k17-1045}
}
```

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- [↑ back to papers of 2017](https://md.research.elamur.ai/free/en/papers/2017/page/2/)

## References

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