TitleGlobal Encoding for Abstractive Summarization
AuthorsLin, Junyang
Sun, Xu
Ma, Shuming
Su, Qi
AffiliationPeking Univ, Sch EECS, MOE Key Lab Computat Linguist, Beijing, Peoples R China.
Peking Univ, Sch Foreign Languages, Beijing, Peoples R China.
Issue Date2018
PublisherPROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2
CitationPROCEEDINGS OF THE 56TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2. 2018, 163-169.
AbstractIn neural abstractive summarization, the conventional sequence-to-sequence (seq2seq) model often suffers from repetition and semantic irrelevance. To tackle the problem, we propose a global encoding framework, which controls the information flow from the encoder to the decoder based on the global information of the source context. It consists of a convolutional gated unit to perform global encoding to improve the representations of the source-side information. Evaluations on the LCSTS and the English Gigaword both demonstrate that our model outperforms the baseline models, and the analysis shows that our model is capable of generating summary of higher quality and reducing repetition(1).
URIhttp://hdl.handle.net/20.500.11897/575108
IndexedCPCI-S(ISTP)
Appears in Collections:信息科学技术学院
计算语言学教育部重点实验室
外国语学院

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