Automatic news comment generation is a new testbed for techniques of natural language generation. 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 datasets indicate that our model can significantly outperform existing methods in terms of both automatic evaluation and human judgment.
@article{arxiv.1909.11974,
title = {Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation},
author = {Ze Yang and Can Xu and Wei Wu and Zhoujun Li},
journal= {arXiv preprint arXiv:1909.11974},
year = {2019}
}