  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.
