{"url":"/dataset/sume","name":"SuMe","full_name":"A Dataset Towards Summarizing Biomedical Mechanisms","description_markdown":"Can language models read biomedical texts and explain the biomedical mechanisms discussed? In this work we introduce a biomedical mechanism summarization task. Biomedical studies often investigate the mechanisms behind how one entity (e.g., a protein or a chemical) affects another in a biological context. The abstracts of these publications often include a focused set of sentences that present relevant supporting statements regarding such relationships, associated experimental evidence, and a concluding sentence that summarizes the mechanism underlying the relationship. We leverage this structure and create a summarization task, where the input is a collection of sentences and the main entities in an abstract, and the output includes the relationship and a sentence that summarizes the mechanism. Using a small amount of manually labeled mechanism sentences, we train a mechanism sentence classifier to filter a large biomedical abstract collection and create a summarization dataset with 22k instances. We also introduce conclusion sentence generation as a pretraining task with 611k instances. We benchmark the performance of large bio-domain language models. We find that while the pretraining task help improves performance, the best model produces acceptable mechanism outputs in only 32% of the instances, which shows the task presents significant challenges in biomedical language understanding and summarization.","description_withheld":null,"homepage":"https://stonybrooknlp.github.io/SuMe/","introduced_date":"2022-05-10","introduced_date_note":null,"introduced_by":{"paper":"/paper/sume-a-dataset-towards-summarizing-biomedical-1","title":"SuMe: A Dataset Towards Summarizing Biomedical Mechanisms","first_author":"Mohaddeseh Bastan","url":null},"license":null,"modalities":[],"tasks":[{"name":"Text Summarization","url":"/task/text-summarization","datasets_with_task":"/datasets/task/text-summarization"},{"name":"Biomedical Information Retrieval","url":"/task/biomedical-information-retrieval","datasets_with_task":"/datasets/task/biomedical-information-retrieval"},{"name":"Explanation Generation","url":"/task/explanation-generation","datasets_with_task":"/datasets/task/explanation-generation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["SuMe"],"data_loaders":[{"repo":"https://github.com/StonyBrookNLP/SuMe","url":"https://github.com/StonyBrookNLP/SuMe","frameworks":["pytorch"]}],"num_papers_in_archive":2,"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."}