TitleCoherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model
AuthorsLi, Wei
Xu, Jingjing
He, Yancheng
Yan, Shengli
Wu, Yunfang
Sun, Xu
AffiliationPeking Univ, MOE Key Lab Computat Linguist, Sch EECS, Beijing, Peoples R China
Tencent, Platform & Content Grp, Shenzhen, Peoples R China
Peking Univ, Beijing Inst Big Data Res, Deep Learning Lab, Beijing, Peoples R China
Issue Date2019
Publisher57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019)
AbstractAutomatic article commenting is helpful in encouraging user engagement and interaction on online news platforms. However, the news documents are usually too long for traditional encoder-decoder based models, which often results in general and irrelevant comments. In this paper, we propose to generate comments with a graph-to-sequence model that models the input news as a topic interaction graph. By organizing the article into graph structure, our model can better understand the internal structure of the article and the connection between topics, which makes it better able to understand the story. We collect and release a large scale news-comment corpus from a popular Chinese online news platform Tencent Kuaibao. 1 Extensive experiment results show that our model can generate much more coherent and informative comments compared with several strong baseline models.(2)
URIhttp://hdl.handle.net/20.500.11897/552799
IndexedISSHP
CPCI-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

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