Generating Pertinent and Diversified Comments with Topic-aware Pointer-Generator Networks
This addresses the challenge of improving comment generation in NLG, which is incremental as it builds on existing pointer-generator networks with topic integration.
The paper tackles the problem of generating pertinent and diverse comments for articles by proposing a Topic-aware Pointer-Generator Network (TPGN) that leverages topic information, resulting in significant outperformance over baseline models on a large dataset.
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.