@inproceedings{sotudeh-gharebagh-etal-2020-guir,
    title = "{GUIR} @ {L}ong{S}umm 2020: Learning to Generate Long Summaries from Scientific Documents",
    author = "Sotudeh Gharebagh, Sajad  and
      Cohan, Arman  and
      Goharian, Nazli",
    booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.sdp-1.41",
    doi = "10.18653/v1/2020.sdp-1.41",
    pages = "356--361",
    abstract = "This paper presents our methods for the LongSumm 2020: Shared Task on Generating Long Summaries for Scientific Documents, where the task is to generatelong summaries given a set of scientific papers provided by the organizers. We explore 3 main approaches for this task: 1. An extractive approach using a BERT-based summarization model; 2. A two stage model that additionally includes an abstraction step using BART; and 3. A new multi-tasking approach on incorporating document structure into the summarizer. We found that our new multi-tasking approach outperforms the two other methods by large margins. Among 9 participants in the shared task, our best model ranks top according to Rouge-1 score (53.11{\%}) while staying competitive in terms of Rouge-2.",
}