Selective Attention Encoders by Syntactic Graph Convolutional Networks for Document Summarization

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.

ICASSP 2020

References in corpus (9)

• Sequence to Sequence Learning with Neural Networks
• A Deep Reinforced Model for Abstractive Summarization
• SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents
• Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
• Selective Encoding for Abstractive Sentence Summarization
• Get To The Point: Summarization with Pointer-Generator Networks
• Structure-Infused Copy Mechanisms for Abstractive Summarization
• Learning End-to-End Goal-Oriented Dialog
• A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss