@inproceedings{gupta-etal-2021-sumpubmed,
    title = "{SumPubMed}: Summarization Dataset of {P}ub{M}ed Scientific Articles",
    author = "Gupta, Vivek  and
      Bharti, Prerna  and
      Nokhiz, Pegah  and
      Karnick, Harish",
    editor = "Kabbara, Jad  and
      Lin, Haitao  and
      Paullada, Amandalynne  and
      Vamvas, Jannis",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2021.acl-srw.30/",
    doi = "10.18653/v1/2021.acl-srw.30",
    pages = "292--303",
    abstract = "Most earlier work on text summarization is carried out on news article datasets. The summary in these datasets is naturally located at the beginning of the text. Hence, a model can spuriously utilize this correlation for summary generation instead of truly learning to summarize. To address this issue, we constructed a new dataset, SumPubMed , using scientific articles from the PubMed archive. We conducted a human analysis of summary coverage, redundancy, readability, coherence, and informativeness on SumPubMed . SumPubMed is challenging because (a) the summary is distributed throughout the text (not-localized on top), and (b) it contains rare domain-specific scientific terms. We observe that seq2seq models that adequately summarize news articles struggle to summarize SumPubMed . Thus, SumPubMed opens new avenues for the future improvement of models as well as the development of new evaluation metrics."
}