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Multi-Document Summarization datasets
archive 2025-07-28
15 datasets carry the task tag "Multi-Document Summarization" (the task itself: Multi-Document Summarization), ordered by the archive's paper count. Page 1 of 1: 15 shown of 15. Facet routes are this site's own (the archive records the tag string, not a page).
The archive holds 12,214 dataset rows; 12,172 are listed. 6 are withheld from every listing and count here as vandalised before snapshot (6 with contact-centre spam in the title, 0 with a spam description on a row that has no homepage, no paper and no papers counted; none with more than 1 paper, 0 with a benchmark), listed in withheld.json; 1 listed row carries a vandalised description, withheld on its page. This gate never withholds a row with a homepage or a paper that resolves, and a clean description; the content rules below withhold a row whose name is spam whatever else it carries. The gate is a phrase list: these are the rows it caught, not a claim that the rest is clean. Before that gate, the site's content rules withhold 36 more rows (invite-code, gambling, travel-booking, contact-centre and similar spam in the name or on a row with nothing real behind it); they have no page and are listed in withheld.json.
Filter 51 task tags shown of 3,717, by dataset count; the full filter by modality, task and language is on /datasets
Multi-Document Summarization datasets 1–15 of 15
Multi-News, consists of news articles and human-written summaries of these articles from the site newser.com.
122 papers · 5 benchmarks
WikiSum is a dataset based on English Wikipedia and suitable for a task of multi-document abstractive summarization.
54 papers · 0 benchmarks
WCEP (Wikipedia Current Events Portal)
The WCEP dataset for multi-document summarization (MDS) consists of short, human-written summaries about news events, obtained from the Wikipedia Current Events Portal (WCEP), each paired with a cluster of news articles associated with an…
31 papers · 1 benchmark
The DUC2004 dataset is a dataset for document summarization.
15 papers · 4 benchmarks
5,519 query-based summaries, each associated with an average of 6 input documents selected from an index of 355M documents from Common Crawl.
11 papers · 0 benchmarks
Multi-XScience is a large-scale dataset for multi-document summarization of scientific articles.
10 papers · 0 benchmarks
OPOSUM is a dataset for the training and evaluation of Opinion Summarization models which contains Amazon reviews from six product domains: Laptop Bags, Bluetooth Headsets, Boots, Keyboards, Televisions, and Vacuums.
7 papers · 0 benchmarks
MATINF (Maternal and Infant Dataset)
Maternal and Infant (MATINF) Dataset is a large-scale dataset jointly labeled for classification, question answering and summarization in the domain of maternity and baby caring in Chinese.
5 papers · 0 benchmarks
MS^2 (Multi-Document Summarization of Medical Studies)
MS^2 (Multi-Document Summarization of Medical Studies) is a dataset of over 470k documents and 20k summaries derived from the scientific literature.
4 papers · 1 benchmark
Wikipedia Generation is a dataset for article generation from Wikipedia from references at the end of Wikipedia page and the top 10 search results for the Wikipedia topic.
4 papers · 0 benchmarks
Healthline is a nutrition related dataset for multi-document summarization, using scientific studies.
3 papers · 0 benchmarks
OpenAsp Dataset OpenAsp is an Open Aspect-based Multi-Document Summarization dataset derived from DUC and MultiNews summarization datasets.
2 papers · 0 benchmarks
This is a dataset for multi-document summarization in Portuguese, what means that it has examples of multiple documents (input) related to human-written summaries (output).
1 paper · 0 benchmarks
GameWikiSum is a domain-specific (video game) dataset for multi-document summarization, which is one hundred times larger than commonly used datasets, and in another domain than news.
1 paper · 0 benchmarks
LSARS (Large Scale Abstractive multi-Review Summarization)
In an active e-commerce environment, customers process a large number of reviews when deciding on whether to buy a product or not.
0 papers · 0 benchmarks
Paper counts and descriptions are the archive's, frozen 2025-07-28; no citation counts, no stars, no trending. Sorting by "most cited" or "newest" was a live-site feature the archive does not carry.