| Challenge: | Existing methods for summarizing documents neglect the relationships between documents . existing methods treat retrieval and summarization as separate tasks . |
| Approach: | They propose a framework that captures global document relationships through graph-based clustering . this cluster-level thematic information is then used to guide large language models . |
| Outcome: | The proposed framework significantly improves retrieval accuracy and produces better summaries than existing methods. |
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| Challenge: | Multi-document summarization (MDS) assumes a set of topic-related documents is provided as input. |
| Approach: | They formalize the task and bootstrap it using existing datasets, retrievers and summarizers. |
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Enhancing Multi-Document Summarization with Cross-Document Graph-based Information Extraction (2023.eacl-main)
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| Challenge: | Information extraction (IE) and summarization (summarization) are closely related, but both aims to abstract the most salient information into a generated text summary. |
| Approach: | They propose to use structured IE graphs to enhance the abstractive summarization task by using cross-document IE output to incorporate an alignment loss between IE nodes and their text spans to reduce inconsistencies. |
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Leveraging Graph to Improve Abstractive Multi-Document Summarization (2020.acl-main)
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| Challenge: | Empirical results show that our model brings substantial improvements over several strong baselines. |
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Topic-Guided Abstractive Multi-Document Summarization (2021.findings-emnlp)
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| Challenge: | Existing studies on multi-document summarization (MDS) focus on extractive and abstractive approaches to create a fluent and concise summary for a collection of thematically related documents. |
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Graph of Records: Boosting Retrieval Augmented Generation for Long-context Summarization with Graphs (2025.acl-long)
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Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward (2020.acl-main)
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| Challenge: | Abstractive summarization models for document encoders suffer from fabricated content and are often near-extractive. |
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GraphLSS: Integrating Lexical, Structural, and Semantic Features for Long Document Extractive Summarization (2025.naacl-short)
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| Challenge: | Graph-based methods for extracting documents have been popular, but they often require external tools or additional machine learning models to define graph components. |
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BASS: Boosting Abstractive Summarization with Unified Semantic Graph (2021.acl-long)
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| Challenge: | Abstractive summarization for long-document or multi-document remains challenging for Seq2Seq as it does not analyze long-distance relations in text. |
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Beyond the Scientific Document: A Citation-Aware Multi-Granular Summarization Approach with Heterogeneous Graphs (2025.findings-emnlp)
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| Challenge: | Experimental results demonstrate that our model outperforms existing approaches for summarizing documents. |
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Multi Graph Neural Network for Extractive Long Document Summarization (2022.coling-1)
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| Challenge: | Heterogeneous Graph Neural Networks (GNN) have been proposed as an emergent approach for extracting document summarization (EDS) but there are still limitations in applying it for long documents due to the lack of inter-sentence connections. |
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