Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation

Ze Yang, Can Xu, Wei Wu, Zhoujun Li


Abstract
Automatic news comment generation is beneficial for real applications but has not attracted enough attention from the research community. In this paper, we propose a “read-attend-comment” procedure for news comment generation and formalize the procedure with a reading network and a generation network. The reading network comprehends a news article and distills some important points from it, then the generation network creates a comment by attending to the extracted discrete points and the news title. We optimize the model in an end-to-end manner by maximizing a variational lower bound of the true objective using the back-propagation algorithm. Experimental results on two public datasets indicate that our model can significantly outperform existing methods in terms of both automatic evaluation and human judgment.
Anthology ID:
D19-1512
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
5077–5089
Language:
URL:
https://aclanthology.org/D19-1512
DOI:
10.18653/v1/D19-1512
Bibkey:
Cite (ACL):
Ze Yang, Can Xu, Wei Wu, and Zhoujun Li. 2019. Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5077–5089, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation (Yang et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/author-url/D19-1512.pdf