{"url":"/dataset/scisummnet","name":"ScisummNet","full_name":null,"description_markdown":"Large-scale manually-annotated corpus for 1,000 scientific papers (on computational linguistics) for automatic summarization. Summaries for each paper are constructed from the papers that cite that paper and from that paper's abstract.\r\nSource: [ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks](https://arxiv.org/pdf/1909.01716v3.pdf)","description_withheld":null,"homepage":"https://cs.stanford.edu/~myasu/projects/scisumm_net/","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/scisummnet-a-large-annotated-dataset-and","title":"ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks","first_author":"Michihiro Yasunaga","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Text Summarization","url":"/task/text-summarization","datasets_with_task":"/datasets/task/text-summarization"},{"name":"Document Summarization","url":"/task/document-summarization","datasets_with_task":"/datasets/task/document-summarization"},{"name":"Scientific Document Summarization","url":"/task/scientific-article-summarization","datasets_with_task":"/datasets/task/scientific-article-summarization"}],"languages":[],"variants":["ScisummNet"],"data_loaders":[],"num_papers_in_archive":20,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}