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

摘要：

　　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 summarys informativeness by explicitly incorporating topical keywords information from the original document into a pointer-generator network via a new attention mechanism so that a topicoriented 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.

关键词： Abstractive summarization Sequence to sequence model Pointer-generator network Topical keywords Attention mechanism

作者: Xiaoping Jiang Po Hu Liwei Hou Xia Wang

作者单位: School of Computer Science,Central China Normal University,Wuhan 430079,China

会议类型: 国际会议

会议名称: 2018自然语言处理与中文计算国际会议(NLPCC2018)

会议地点: 呼和浩特

会议语种:英文

页码: 464-474

在线出版日期: 2018-08-26（万方平台首次上网日期，不代表论文的发表时间）