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

TL;DR

This paper introduces a novel Topic-aware Pointer-Generator Network model that leverages article topic information to generate more relevant and diverse comments, significantly improving over existing methods.

Contribution

The paper presents a new model integrating topic information into pointer-generator networks for improved comment generation.

Findings

The model produces more pertinent comments.

It achieves higher diversity in generated comments.

Outperforms baseline models significantly.

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.