BiSET: Bi-directional Selective Encoding with Template for Abstractive
Summarization
Abstract
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