mlsanityData to 7 Oct 2026

PeerSum: A Peer Review Dataset for Abstractive Multi-document Summarization

arXiv 2203.01769 ↗First posted 3 Mar 2022cs.IR cs.CLcited by 2

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Abstract (arXiv)

We present PeerSum, a new MDS dataset using peer reviews of scientific publications. Our dataset differs from the existing MDS datasets in that our summaries (i.e., the meta-reviews) are highly abstractive and they are real summaries of the source documents (i.e., the reviews) and it also features disagreements among source documents. We found that current state-of-the-art MDS models struggle to generate high-quality summaries for PeerSum, offering new research opportunities.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 13 of its 23 references that are in the map. First posted on arXiv 3 Mar 2022.

Builds on

13 of 23 references.

References · 13

  1. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  2. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
  3. 2019Text Summarization with Pretrained Encoderscited by 553
  4. 2019Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Modelcited by 431
  5. 2018Generating Wikipedia by Summarizing Long Sequencescited by 333
  6. 2018A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applicationscited by 135
  7. 2019Hierarchical Transformers for Multi-Document Summarizationcited by 132
  8. 2020Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articlescited by 96
  9. 2020A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portalcited by 72
  10. 2020SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarizationcited by 69
  11. 2020Leveraging Graph to Improve Abstractive Multi-Document Summarizationcited by 67
  12. 2020Multi-document Summarization via Deep Learning Techniques: A Surveycited by 58
1 more
  1. 2020FFCI: A Framework for Interpretable Automatic Evaluation of Summarizationcited by 24

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Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2025How Far Are AI Scientists from Changing the World?cited by 15
  2. 2024M2DS: Multilingual Dataset for Multi-document Summarisationcited by 3