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 in...
