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Extreme Summarization datasets

archive 2025-07-28

7 datasets carry the task tag "Extreme Summarization" (the task itself: Extreme Summarization), ordered by the archive's paper count. Page 1 of 1: 7 shown of 7. 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

Extreme Summarization datasets 1–7 of 7

GEM (Generation, Evaluation, and Metrics)
Generation, Evaluation, and Metrics (GEM) is a benchmark environment for Natural Language Generation with a focus on its Evaluation, both through human annotations and automated Metrics.
45 papers · 1 benchmark
The Extreme Summarization (XSum) dataset is a dataset for evaluation of abstractive single-document summarization systems.
32 papers · 5 benchmarks
A new multi-target dataset of 5.4K TLDRs over 3.2K papers.
13 papers · 0 benchmarks
CiteSum is a large-scale scientific extreme summarization benchmark.
4 papers · 1 benchmark
An open corpus of Scientific Research papers which has a representative sample from across scientific disciplines.
2 papers · 0 benchmarks
TLDR9+ is a large-scale summarization dataset containing over 9 million training instances extracted from Reddit discussion forum.
2 papers · 1 benchmark
SummZoo, a benchmark consists of 8 diverse summarization tasks with multiple sets of few-shot samples for each task, covering both monologue and dialogue domains.
1 paper · 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.