  Previous work for text summarization in scientific domain mainly focused on
the content of the input document, but seldom considering its citation network.
However, scientific papers are full of uncommon domain-specific terms, making
it almost impossible for the model to understand its true meaning without the
help of the relevant research community. In this paper, we redefine the task of
scientific papers summarization by utilizing their citation graph and propose a
citation graph-based summarization model CGSum which can incorporate the
information of both the source paper and its references. In addition, we
construct a novel scientific papers summarization dataset Semantic Scholar
Network (SSN) which contains 141K research papers in different domains and 661K
citation relationships. The entire dataset constitutes a large connected
citation graph. Extensive experiments show that our model can achieve
competitive performance when compared with the pretrained models even with a
simple architecture. The results also indicates the citation graph is crucial
to better understand the content of papers and generate high-quality summaries.
