  Comment generation, a new and challenging task in Natural Language Generation
(NLG), attracts a lot of attention in recent years. However, comments generated
by previous work tend to lack pertinence and diversity. In this paper, we
propose a novel generation model based on Topic-aware Pointer-Generator
Networks (TPGN), which can utilize the topic information hidden in the articles
to guide the generation of pertinent and diversified comments. Firstly, we
design a keyword-level and topic-level encoder attention mechanism to capture
topic information in the articles. Next, we integrate the topic information
into pointer-generator networks to guide comment generation. Experiments on a
large scale of comment generation dataset show that our model produces the
valuable comments and outperforms competitive baseline models significantly.
