多文档短摘要生成技术研究

doi:10.16088/j.issn.1001-6600.2019.02.008

Abstract

Abstract: The automatic summarization technique is used to compress a long piece of articles into a shorter text that can generalize the content of the original text. Multiple documents are highly redundant, while the space of electronic devices is limited. Thus, the development of abstracts has faced with challenges. In this paper,a rough granularity sorting method with convolution features is proposed. First,the similarity matrix between sentences is regarded as a topological graph,and the convolution features of the graph convolution are obtained. Then,the convolution features of the graph convolution are fused and the mainstream extraction type multi document summarization technology is used to repeat the sentence,the top four sentences are selected as summaries. Lastly,a short summary generation model based on Seq2seq framework is proposed:1)the method based on the convolution neural network (CNN) is adopted in the encoder part;2)the pointer mechanism based on attention is introduced,the subject vector is incorporated into it. A series of experimental results show that in this scenario,compared with recurrent neural network (RNN),the encoder part based on CNN can be better parallelized,thus increasing the efficiency significantly on the premise of basically consistent effect. In addition, compared with the traditional extraction and compression model, the model proposed in this paper has been significantly improved in ROUGE indicators and readability (information and fluency).

Key words: multi-document, short summary generation, Seq2seq

CLC Number:

TP391

ZHANG Suiyuan, XUE Yuanhai, YU Xiaoming, LIU Yue, CHENG Xueqi. Research on Short Summary Generation of Multi-Document[J].Journal of Guangxi Normal University(Natural Science Edition), 2019, 37(2): 60-74.

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