Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model
Open accessAbstract
Automatic generation of summaries from multiple news articles is a valuable tool as the number of online publications grows rapidly.Single document summarization (SDS) systems have benefited from advances in neural encoder-decoder model thanks to the availability of large datasets.However, multidocument summarization (MDS) of news articles has been limited to datasets of a couple of hundred examples.In this paper, we introduce Multi-News, the first large-scale MDS news dataset.Additionally, we propose an end-to-end model which incorporates a traditional extractive summarization model with a standard SDS model and achieves competitive results on MDS datasets.We benchmark several methods on Multi-News and release our data and code in hope that this work will promote advances in summarization in the multidocument setting 1 .
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Each summary in the original Multi-News dataset is derived from multiple short news articles.
“Each summary in the original Multi-News dataset is derived from multiple short news articles.”
From L-Eval: Instituting Standardized Evaluation for Long Context Language Models (2024) · cites this paper as Fabbri et al., 2019
Section 3.1 says each Multi-News summary has 2–10 source documents, supporting the claim that summaries draw on multiple news articles. It does not characterize those articles as short; its length comparison concerns the concatenated input, not each article. Page numbers were not provided in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredEarly attempts to generate reviews often addressed a different problem, such as multi-document summarization of existing reviews, or relied on citation networks rather than generating novel critique directly from a source manuscript.
“Early attempts to generate reviews often addressed a different problem, such as the multi-document summarization of existing reviews (Li et al., 2022;Fabbri et al., 2019), or relied on citation networks rather than generating novel critique directly from a source manuscript (Li and Zhuge, 2021).”
From Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future (2026) · cites this paper as Fabbri et al., 2019
Section 3 (Multi-News Dataset; page not supplied) identifies this as a multi-document summarization dataset, but its inputs are news articles and its target summaries are professionally written news summaries. Section 3.3 mentions product-review summarization only as related work. Fabbri et al. therefore support the multi-document summarization part, not the characterization of their work as summarizing existing reviews. (LLM, conf=0.87)
Experimental — accuracy not yet measuredMulti-News contains news articles and human-written summaries compiled from newser.com.
“This dataset is sourced from Multi-News (Fabbri et al., 2019), which contains news articles and human-written summaries compiled from newser.com.”
From L-Eval: Instituting Standardized Evaluation for Long Context Language Models (2024) · cites this paper as Fabbri et al., 2019
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“We consider existing summarization datasets with an average summary length of at least 150 words, which includes several popular datasets like arXiv (Cohan et al., 2018), Bill-Sum (Kornilova and Eidelman, 2019) and Multi-News (Fabbri et al., 2019); see Table 1 for a full list.”
BackgroundMethod
- Authors
- Fabbri, A., Li, I., She, T., Li, S., & Radev, D.
- Affiliations
- Yale University (US)
- Venue
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Published
- 2019
- Pages
- 1074-1084
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2948120406
- Cited by
- 449
- References
- 1
- Citations by year
- 2026: 31 · 2025: 43 · 2024: 47 · 2023: 114 · 2022: 59 · 2021: 109 · 2020: 42 · 2019: 4
- FWCI
- 20.47
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- multi-document summarization, news articles, abstractive summarization, extractive summarization, single-document summarization
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