  Text summarization aims to generate a headline or a short summary consisting
of the major information of the source text. Recent studies employ the
sequence-to-sequence framework to encode the input with a neural network and
generate abstractive summary. However, most studies feed the encoder with the
semantic word embedding but ignore the syntactic information of the text.
Further, although previous studies proposed the selective gate to control the
information flow from the encoder to the decoder, it is static during the
decoding and cannot differentiate the information based on the decoder states.
In this paper, we propose a novel neural architecture for document
summarization. Our approach has the following contributions: first, we
incorporate syntactic information such as constituency parsing trees into the
encoding sequence to learn both the semantic and syntactic information from the
document, resulting in more accurate summary; second, we propose a dynamic gate
network to select the salient information based on the context of the decoder
state, which is essential to document summarization. The proposed model has
been evaluated on CNN/Daily Mail summarization datasets and the experimental
results show that the proposed approach outperforms baseline approaches.
