Coherent Comments Generation for Chinese Articles with a Graph-to-Sequence Model

Wei Li, Jingjing Xu, Yancheng He, ShengLi Yan, Yunfang Wu, Xu Sun


Abstract
Automatic article commenting is helpful in encouraging user engagement on online news platforms. However, the news documents are usually too long for models under traditional encoder-decoder frameworks, 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 generate coherent and informative comments. We collect and release a large scale news-comment corpus from a popular Chinese online news platform Tencent Kuaibao. Extensive experiment results show that our model can generate much more coherent and informative comments compared with several strong baseline models.
Anthology ID:
P19-1479
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4843–4852
Language:
URL:
https://aclanthology.org/P19-1479
DOI:
10.18653/v1/P19-1479
Bibkey:
Cite (ACL):
Wei Li, Jingjing Xu, Yancheng He, ShengLi Yan, Yunfang Wu, and Xu Sun. 2019. Coherent Comments Generation for Chinese Articles with a Graph-to-Sequence Model. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4843–4852, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Coherent Comments Generation for Chinese Articles with a Graph-to-Sequence Model (Li et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1479.pdf
Video:
 https://vimeo.com/385264795
Code
 lancopku/Graph-to-seq-comment-generation