@inproceedings{yang-etal-2019-read,
title = "Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation",
author = "Yang, Ze and
Xu, Can and
Wu, Wei and
Li, Zhoujun",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "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 = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1512",
doi = "10.18653/v1/D19-1512",
pages = "5077--5089",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation
%A Yang, Ze
%A Xu, Can
%A Wu, Wei
%A Li, Zhoujun
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F yang-etal-2019-read
%X 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.
%R 10.18653/v1/D19-1512
%U https://aclanthology.org/D19-1512
%U https://doi.org/10.18653/v1/D19-1512
%P 5077-5089
Markdown (Informal)
[Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation](https://aclanthology.org/D19-1512) (Yang et al., EMNLP-IJCNLP 2019)
ACL