%0 Conference Proceedings %T Dimsum @LaySumm 20 %A Yu, Tiezheng %A Su, Dan %A Dai, Wenliang %A Fung, Pascale %Y Chandrasekaran, Muthu Kumar %Y de Waard, Anita %Y Feigenblat, Guy %Y Freitag, Dayne %Y Ghosal, Tirthankar %Y Hovy, Eduard %Y Knoth, Petr %Y Konopnicki, David %Y Mayr, Philipp %Y Patton, Robert M. %Y Shmueli-Scheuer, Michal %S Proceedings of the First Workshop on Scholarly Document Processing %D 2020 %8 November %I Association for Computational Linguistics %C Online %F yu-etal-2020-dimsum %X 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. %R 10.18653/v1/2020.sdp-1.35 %U https://aclanthology.org/2020.sdp-1.35 %U https://doi.org/10.18653/v1/2020.sdp-1.35 %P 303-309