Study on Academic Documents -Oriented Automatic Summarization of Short Texts

摘要：

　　Traditional automatic text summarization relies heavily on the original text information,and the extensibility is limited.However,generation-style abstractive methods attempt to generate the corresponding summarization by understanding the original semantics.We set out to set up a sequence-to-sequence model for academic document summarization generation.For purpose of reducing the detail loss of input sequence information,we put forward the attention mechanism to assign the weight of each input word.We trained this model on Chinese literature data set.It generated a reliable document summary.Our test shows that the approach has good adaptability to Chinese academic literature and has good performance in text summarization.

作者: Chunxiao Gao Bo Guan Xiaoyue Zhang Hao Liu Zhiqiang Wei

作者单位: Department of Computer Science and Technology,Ocean University of China,Qingdao,China Department of Computer Science and Technology,Ocean University of China,Qingdao,China;Pilot National

会议类型: 国际会议

会议名称: The 2nd International Symposium on Application of Materials Science and Energy Materials (SAMSE 2018) 第二届材料科学应用与能源材料国际研讨会2018年

会议地点: 上海

会议语种:英文

页码: 1-7

在线出版日期: 2018-12-17（万方平台首次上网日期，不代表论文的发表时间）