mlsanityData to 7 Oct 2026

AgreeSum: Agreement-Oriented Multi-Document Summarization

arXiv 2106.02278 ↗First posted 4 Jun 2021cs.CLcited by 10

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Abstract (arXiv)

We aim to renew interest in a particular multi-document summarization (MDS) task which we call AgreeSum: agreement-oriented multi-document summarization. Given a cluster of articles, the goal is to provide abstractive summaries that represent information common and faithful to all input articles. Given the lack of existing datasets, we create a dataset for AgreeSum, and provide annotations on article-summary entailment relations for a subset of the clusters in the dataset. We aim to create strong baselines for the task by applying the top-performing pretrained single-document summarization model PEGASUS onto AgreeSum, leveraging both annotated clusters by supervised losses, and unannotated clusters by T5-based entailment-related and language-related losses. Compared to other baselines, both automatic evaluation and human evaluation show better article-summary and cluster-summary entailment in generated summaries. On a separate note, we hope that our article-summary entailment annotations contribute to the community's effort in improving abstractive summarization faithfulness.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 35 of its 55 references that are in the map. First posted on arXiv 4 Jun 2021.

Builds on

35 of 55 references.

References · 35

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  12. 2015A Neural Attention Model for Abstractive Sentence Summarizationcited by 394
23 more
  1. 2020Asking and Answering Questions to Evaluate the Factual Consistency of Summariescited by 376
  2. 2018Generating Wikipedia by Summarizing Long Sequencescited by 333
  3. 2020FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarizationcited by 292
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  8. 2020Detecting Hallucinated Content in Conditional Neural Sequence Generationcited by 151
  9. 2019Hierarchical Transformers for Multi-Document Summarizationcited by 132
  10. 2020Intermediate-Task Transfer Learning with Pretrained Models for Natural Language Understanding: When and Why Does It Work?cited by 127
  11. 2020On Exposure Bias, Hallucination and Domain Shift in Neural Machine Translationcited by 127
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  13. 2020Heterogeneous Graph Neural Networks for Extractive Document Summarizationcited by 90
  14. 2020A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portalcited by 72
  15. 2020Leveraging Graph to Improve Abstractive Multi-Document Summarizationcited by 67
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  20. 2020Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement Learningcited by 28
  21. 2017Adversarial Neural Machine Translationcited by 21
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  23. 2019SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoderscited by 7

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Cited by year

Citing papers per year: 2021: 1; 2022: 4; 2023: 3; 2024: 0; 2025: 2.

10 papers.

  1. 2021ExT5: Towards Extreme Multi-Task Scaling for Transfer Learningcited by 161
  2. 2023Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedbackcited by 71
  3. 2022SQuALITY: Building a Long-Document Summarization Dataset the Hard Waycited by 50
  4. 2022Reward Gaming in Conditional Text Generationcited by 29
  5. 2022NeuS: Neutral Multi-News Summarization for Mitigating Framing Biascited by 26
  6. 2022Improving Multi-Document Summarization through Referenced Flexible Extraction with Credit-Awarenesscited by 13
  7. 2023Tackling Hallucinations in Neural Chart Summarizationcited by 6
  8. 2025Multi-document Summarization through Multi-document Event Relation Graph Reasoning in LLMs: a case study in Framing Bias Mitigationcited by 2
  9. 2023Leveraging Deep Learning for Abstractive Code Summarization of Unofficial Documentationcited by 0
  10. 2025A Unified Retrieval Framework with Document Ranking and EDU Filtering for Multi-document Summarizationcited by 0