@inproceedings{umapathy-etal-2020-citeqa,
    title = "{C}ite{QA}@{CLS}ci{S}umm 2020",
    author = "Umapathy, Anjana  and
      Radhakrishnan, Karthik  and
      Jain, Kinjal  and
      Singh, Rahul",
    editor = "Chandrasekaran, Muthu Kumar  and
      de Waard, Anita  and
      Feigenblat, Guy  and
      Freitag, Dayne  and
      Ghosal, Tirthankar  and
      Hovy, Eduard  and
      Knoth, Petr  and
      Konopnicki, David  and
      Mayr, Philipp  and
      Patton, Robert M.  and
      Shmueli-Scheuer, Michal",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.sdp-1.34/",
    doi = "10.18653/v1/2020.sdp-1.34",
    pages = "297--302",
    abstract = "In academic publications, citations are used to build context for a concept by highlighting relevant aspects from reference papers. Automatically identifying referenced snippets can help researchers swiftly isolate principal contributions of scientific works. In this paper, we exploit the underlying structure of scientific articles to predict reference paper spans and facets corresponding to a citation. We propose two methods to detect citation spans - keyphrase overlap, BERT along with structural priors. We fine-tune FastText embeddings and leverage textual, positional features to predict citation facets."
}