{"url":"/dataset/meqsum","name":"MeQSum","full_name":null,"description_markdown":"**MeQSum** is a dataset for medical question summarization. It contains 1,000 summarized consumer health questions.\n\nSource: [https://www.aclweb.org/anthology/P19-1215.pdf](https://www.aclweb.org/anthology/P19-1215.pdf)\nImage Source: [https://www.aclweb.org/anthology/P19-1215.pdf](https://www.aclweb.org/anthology/P19-1215.pdf)","description_withheld":null,"homepage":"https://github.com/abachaa/MeQSum","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/on-the-summarization-of-consumer-health","title":"On the Summarization of Consumer Health Questions","first_author":"Asma Ben Abacha","url":null},"license":null,"modalities":[{"name":"Texts","url":"/datasets/modality/texts"},{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[{"name":"Question Answering","url":"/task/question-answering","datasets_with_task":"/datasets/task/question-answering"},{"name":"Text Summarization","url":"/task/text-summarization","datasets_with_task":"/datasets/task/text-summarization"},{"name":"Data Augmentation","url":"/task/data-augmentation","datasets_with_task":"/datasets/task/data-augmentation"}],"languages":[],"variants":["MeQSum"],"data_loaders":[{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/albertvillanova/meqsum","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/abachaa/MeQSum","url":"https://github.com/abachaa/MeQSum","frameworks":[]}],"num_papers_in_archive":33,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/text-summarization-on-meqsum","task":"Text Summarization","dataset_variant":"MeQSum","rows":1,"metrics":["RougeL"],"first_row_in_archive_order":{"model":"BiomedGPT","paper":"/paper/biomedgpt-a-unified-and-generalist-biomedical","metrics":{"RougeL":"52.3"},"code_links":[{"title":"taokz/biomedgpt","url":"https://github.com/taokz/biomedgpt"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/biomedgpt-a-unified-and-generalist-biomedical","title":"BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks","date":"2023-05-26","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":1,"samples_unverified":7,"pointer_only_for_licence":7,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":8,"samples_ran":1,"samples_unverified":7,"pointer_only_for_licence":7,"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."}