Improving Pointer-Generator Network with Keywords Information for Chinese Abstractive Summarization

Abstract

Recently sequence-to-sequence (Seq2Seq) model and its variants are widely used in multiple summarization tasks e.g., sentence compression, headline generation, single document summarization, and have achieved significant performance. However, most of the existing models for abstractive summarization suffer from some undesirable shortcomings such as generating inaccurate contents or insufficient summary. To alleviate the problem, we propose a novel approach to improve the summary’s informativeness by explicitly incorporating topical keywords information from the original document into a pointer-generator network via a new attention mechanism so that a topic-oriented summary can be generated in a context-aware manner with guidance. Preliminary experimental results on the NLPCC 2018 Chinese document summarization benchmark dataset have demonstrated the effectiveness and superiority of our approach. We have achieved significant performance close to that of the best performing system in all the participating systems.