# Leveraging Graph to Improve Abstractive Multi-Document Summarization

arXiv: https://arxiv.org/abs/2005.10043 · HTML: https://mlsanity.com/paper/2005.10043/

## Key facts

- Cited by 67 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2020.

## Built on by (most cited first, 30 of 67)

- [Retrieval-Augmented Generation with Graphs (GraphRAG)](https://mlsanity.com/paper/2501.00309/) (2024)
- [MS2: Multi-Document Summarization of Medical Studies](https://mlsanity.com/paper/2104.06486/) (2021)
- [Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization](https://mlsanity.com/paper/2305.13091/) (2023)
- [PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization](https://mlsanity.com/paper/2110.08499/) (2021)
- [FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations](https://mlsanity.com/paper/2204.06508/) (2022)
- [Graph Transformers: A Survey](https://mlsanity.com/paper/2407.09777/) (2024)
- [Summarizing Multiple Documents with Conversational Structure for Meta-Review Generation](https://mlsanity.com/paper/2305.01498/) (2023)
- [BASS: Boosting Abstractive Summarization with Unified Semantic Graph](https://mlsanity.com/paper/2105.12041/) (2021)
- [Open Domain Multi-document Summarization: A Comprehensive Study of Model Brittleness under Retrieval](https://mlsanity.com/paper/2212.10526/) (2022)
- [HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization](https://mlsanity.com/paper/2110.06388/) (2021)
- [Improving Multi-Document Summarization through Referenced Flexible Extraction with Credit-Awareness](https://mlsanity.com/paper/2205.01889/) (2022)
- [Fooling MOSS Detection with Pretrained Language Models](https://mlsanity.com/paper/2201.07406/) (2022)
- [Multi-Document Scientific Summarization from a Knowledge Graph-Centric View](https://mlsanity.com/paper/2209.04319/) (2022)
- [AgreeSum: Agreement-Oriented Multi-Document Summarization](https://mlsanity.com/paper/2106.02278/) (2021)
- [Chunk, Align, Select: A Simple Long-sequence Processing Method for Transformers](https://mlsanity.com/paper/2308.13191/) (2023)
- [SgSum: Transforming Multi-document Summarization into Sub-graph Selection](https://mlsanity.com/paper/2110.12645/) (2021)
- [Automatic Related Work Generation: A Meta Study](https://mlsanity.com/paper/2201.01880/) (2022)
- [PDSum: Prototype-driven Continuous Summarization of Evolving Multi-document Sets Stream](https://mlsanity.com/paper/2302.05550/) (2023)
- [StrucSum: Graph-Structured Reasoning for Long Document Extractive Summarization with LLMs](https://mlsanity.com/paper/2505.22950/) (2025)
- [The Current State of Summarization](https://mlsanity.com/paper/2305.04853/) (2023)
- [TWAG: A Topic-Guided Wikipedia Abstract Generator](https://mlsanity.com/paper/2106.15135/) (2021)
- [Causal Intervention for Abstractive Related Work Generation](https://mlsanity.com/paper/2305.13685/) (2023)
- [An Efficient Coarse-to-Fine Facet-Aware Unsupervised Summarization Framework based on Semantic Blocks](https://mlsanity.com/paper/2208.08253/) (2022)
- [Enhancing Cross-Document Event Coreference Resolution by Discourse Structure and Semantic Information](https://mlsanity.com/paper/2406.15990/) (2024)
- [Precisely the Point: Adversarial Augmentations for Faithful and Informative Text Generation](https://mlsanity.com/paper/2210.12367/) (2022)
- [Compressed Heterogeneous Graph for Abstractive Multi-Document Summarization](https://mlsanity.com/paper/2303.06565/) (2023)
- [Absformer: Transformer-based Model for Unsupervised Multi-Document Abstractive Summarization](https://mlsanity.com/paper/2306.04787/) (2023)
- [ODSum: New Benchmarks for Open Domain Multi-Document Summarization](https://mlsanity.com/paper/2309.08960/) (2023)
- [Large Language Models Meet Graph Neural Networks: A Perspective of Graph Mining](https://mlsanity.com/paper/2412.19211/) (2024)
- [MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News Summarization](https://mlsanity.com/paper/2109.10650/) (2021)

## Abstract (arXiv)

Graphs that capture relations between textual units have great benefits for detecting salient information from multiple documents and generating overall coherent summaries. In this paper, we develop a neural abstractive multi-document summarization (MDS) model which can leverage well-known graph representations of documents such as similarity graph and discourse graph, to more effectively process multiple input documents and produce abstractive summaries. Our model utilizes graphs to encode documents in order to capture cross-document relations, which is crucial to summarizing long documents. Our model can also take advantage of graphs to guide the summary generation process, which is beneficial for generating coherent and concise summaries. Furthermore, pre-trained language models can be easily combined with our model, which further improve the summarization performance significantly. Empirical results on the WikiSum and MultiNews dataset show that the proposed architecture brings substantial improvements over several strong baselines.