  Previous abstractive methods apply sequence-to-sequence structures to
generate summary without a module to assist the system to detect vital mentions
and relationships within a document. To address this problem, we utilize
semantic graph to boost the generation performance. Firstly, we extract
important entities from each document and then establish a graph inspired by
the idea of distant supervision \citep{mintz-etal-2009-distant}. Then, we
combine a Bi-LSTM with a graph encoder to obtain the representation of each
graph node. A novel neural decoder is presented to leverage the information of
such entity graphs. Automatic and human evaluations show the effectiveness of
our technique.
