@inproceedings{yamada-etal-2020-sequential,
    title = "Sequential Span Classification with Neural Semi-{M}arkov {CRF}s for Biomedical Abstracts",
    author = "Yamada, Kosuke  and
      Hirao, Tsutomu  and
      Sasano, Ryohei  and
      Takeda, Koichi  and
      Nagata, Masaaki",
    editor = "Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.findings-emnlp.77/",
    doi = "10.18653/v1/2020.findings-emnlp.77",
    pages = "871--877",
    abstract = "Dividing biomedical abstracts into several segments with rhetorical roles is essential for supporting researchers' information access in the biomedical domain. Conventional methods have regarded the task as a sequence labeling task based on sequential sentence classification, i.e., they assign a rhetorical label to each sentence by considering the context in the abstract. However, these methods have a critical problem: they are prone to mislabel longer continuous sentences with the same rhetorical label. To tackle the problem, we propose sequential span classification that assigns a rhetorical label, not to a single sentence but to a span that consists of continuous sentences. Accordingly, we introduce Neural Semi-Markov Conditional Random Fields to assign the labels to such spans by considering all possible spans of various lengths. Experimental results obtained from PubMed 20k RCT and NICTA-PIBOSO datasets demonstrate that our proposed method achieved the best micro sentence-F1 score as well as the best micro span-F1 score."
}