Selective Attention Encoders by Syntactic Graph Convolutional Networks for Document Summarization

• URL: http://arxiv.org/abs/2003.08004v1
• Date: Wed, 18 Mar 2020 01:30:02 GMT
• Title: Selective Attention Encoders by Syntactic Graph Convolutional Networks for Document Summarization
• Authors: Haiyang Xu, Yun Wang, Kun Han, Baochang Ma, Junwen Chen, Xiangang Li
• Abstract summary: We propose 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 proposed GCNs based selective attention approach outperforms the baselines and achieves the state-of-the-art performance on the dataset.
• Score: 21.351111598564987
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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