  Abstractive text summarization is a challenging task, and one need to design
a mechanism to effectively extract salient information from the source text and
then generate a summary. A parsing process of the source text contains critical
syntactic or semantic structures, which is useful to generate more accurate
summary. However, modeling a parsing tree for text summarization is not trivial
due to its non-linear structure and it is harder to deal with a document that
includes multiple sentences and their parsing trees. In this paper, we propose
to use a graph to connect the parsing trees from the sentences in a document
and utilize the stacked graph convolutional networks (GCNs) to learn the
syntactic representation for a document. The selective attention mechanism is
used to extract salient information in semantic and structural aspect and
generate an abstractive summary. We evaluate our approach on the CNN/Daily Mail
text summarization dataset. The experimental results show that the proposed
GCNs based selective attention approach outperforms the baselines and achieves
the state-of-the-art performance on the dataset.
