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

arXiv: https://arxiv.org/abs/2005.04396 · HTML: https://mlsanity.com/paper/2005.04396/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2020.

## Built on by (most cited first, 1 of 1)

- [Generating Diversified Comments via Reader-Aware Topic Modeling and Saliency Detection](https://mlsanity.com/paper/2102.06856/) (2021)

## Abstract (arXiv)

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.