@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://preview.aclanthology.org/add-emnlp-2024-awards/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 {\textquotedblleft}read-attend-comment{\textquotedblright} 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."
}