Generating Pertinent and Diversified Comments with Topic-aware Pointer-Generator Networks

• URL: http://arxiv.org/abs/2005.04396v1
• Date: Sat, 9 May 2020 09:04:09 GMT
• Title: Generating Pertinent and Diversified Comments with Topic-aware Pointer-Generator Networks
• Authors: Junheng Huang, Lu Pan, Kang Xu, Weihua Peng, Fayuan Li
• Abstract summary: We propose a novel generation model based on Topic-aware Pointer-Generator Networks (TPGN) We design a keyword-level and topic-level encoder attention mechanism to capture topic information in the articles. We integrate the topic information into pointer-generator networks to guide comment generation.
• Score: 5.046104800241757
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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