  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 the 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.
