@inproceedings{yu-etal-2020-dimsum,
    title = "Dimsum @{L}ay{S}umm 20",
    author = "Yu, Tiezheng  and
      Su, Dan  and
      Dai, Wenliang  and
      Fung, Pascale",
    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-1.35/",
    doi = "10.18653/v1/2020.sdp-1.35",
    pages = "303--309",
    abstract = "Lay summarization aims to generate lay summaries of scientific papers automatically. It is an essential task that can increase the relevance of science for all of society. In this paper, we build a lay summary generation system based on BART model. We leverage sentence labels as extra supervision signals to improve the performance of lay summarization. In the CL-LaySumm 2020 shared task, our model achieves 46.00 Rouge1-F1 score."
}