Selective Attention Encoders by Syntactic Graph Convolutional Networks for Document Summarization
This addresses the challenge of generating accurate summaries from documents with complex syntactic structures, though it is incremental as it builds on existing graph-based methods.
The paper tackles abstractive text summarization by using graph convolutional networks to model syntactic structures from parsing trees across sentences, achieving state-of-the-art performance on the CNN/Daily Mail dataset.
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.