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.
@article{arxiv.2005.04396,
title = {Generating Pertinent and Diversified Comments with Topic-aware Pointer-Generator Networks},
author = {Junheng Huang and Lu Pan and Kang Xu and Weihua Peng and Fayuan Li},
journal= {arXiv preprint arXiv:2005.04396},
year = {2020}
}