Conceptual Multi-Layer Neural Network Model for Headline Generation

摘要：

　　Neural attention-based models have been widely used recently in head-line generation by mapping source document to target headline.However,the traditional neural headline generation models utilize the first sentence of the doc-ument as the training input while ignoring the impact of the document concept information on headline generation.In this work,A new neural attention-based model called concept sensitive neural headline model is proposed,which con-nects the concept information of the document to input text for headline genera-tion and achieves satisfactory results.Besides,we use a multi-layer Bi-LSTM in encoder instead of single layer.Experiments have shown that our model outper-forms state-of-the-art systems on DUC-2004 and Gigaword test sets.

关键词： Attention-based Concept Multi-layer Bi-LSTM

作者: Yidi Guo Heyan Huang Yang Gao Chi Lu

作者单位: Beijing Institute of TechnologyBeijing,China;Beijing Advanced Innovation Center for Imaging Technolo Beijing Institute of TechnologyBeijing,China;Beijing Engineering Research Center of High Volume Lang

会议类型: 国内会议

会议名称: 第十六届全国计算语言学学术会议暨第五届基于自然标注大数据的自然语言处理国际学术研讨会

会议地点: 南京

会议语种:英文

页码: 1-12

在线出版日期: 2017-10-13（万方平台首次上网日期，不代表论文的发表时间）