一种融合关键词的生成式摘要方法  

Abstractive summarization method fused with keywords

作　　者：李伯涵 李红莲[1] Li Bohan;Li Honglian(School of Information&Communication Engineering,Beijing Information Science&Technology University,Beijing 100101,China)

机构地区：[1]北京信息科技大学信息与通信工程学院,北京100101

出　　处：《计算机应用研究》2021年第11期3289-3292,3358,共5页Application Research of Computers

基　　金：国家自然科学基金资助项目(61671070);国家语委重点资助项目(ZDI135-53);自然科学重点实验室基金资助项目(6142006190301);北京信息科技大学校促进内涵重点培育项目(5211910940);北京信息科技大学“勤信人才”培育计划资助项目(QXTCPB201908)。

摘　　要：针对生成式文本摘要中模型对文本语义了解不够充分以及生成摘要缺乏关键信息的问题,提出一种融合关键词的中文摘要生成模型KBPM(Key-BERT-Pen model)。首先使用TextRank方法将文本中关键词抽取出来,然后将抽取出的关键词与原文一起经过BERT预训练模型得到更加精确的上下文表示,最终将得到的词向量输入到带有双重注意力机制的指针模型中,指针模型从词汇表或原文中取出词汇来生成最终的摘要。实验结果表明,KBPM模型能够生成可读性更好、ROUGE分数更高的文本摘要。通过对比分析也验证了KBPM模型有效解决了生成摘要中缺乏关键信息的问题。According to the problem that the model in the generative text summarization doesn’t fully understand the semantics of the text and the generated summary lacks key information,this paper proposed a Key-BERT-Pen model that integrated keywords in Chinese abstract generation.The KBPM first used the TextRank method to extract the keywords in the text,and then obtained a more accurate contextual representation through the BERT pre-training model using the extracted keywords and the original text.Finally,it input the obtained word vector into a pointer model with a dual attention mechanism,and the pointer model took vocabularies from the vocabulary or original text to generate the final abstract.Experimental results show that the KBPM can generate text summaries with better readability and higher ROUGE scores.The comparative analysis also verifies that the KBPM effectively solves the problem of the lack of key information in the generated abstract.

关 键 词：文本摘要 BERT预训练模型 主题关键词 双重注意力机制 指针模型 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]