  The success of neural summarization models stems from the meticulous
encodings of source articles. To overcome the impediments of limited and
sometimes noisy training data, one promising direction is to make better use of
the available training data by applying filters during summarization. In this
paper, we propose a novel Bi-directional Selective Encoding with Template
(BiSET) model, which leverages template discovered from training data to softly
select key information from each source article to guide its summarization
process. Extensive experiments on a standard summarization dataset were
conducted and the results show that the template-equipped BiSET model manages
to improve the summarization performance significantly with a new state of the
art.
