# Automatic Article Commenting: the Task and Dataset

arXiv: https://arxiv.org/abs/1805.03668 · HTML: https://mlsanity.com/paper/1805.03668/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2018.

## Built on by (most cited first, 7 of 7)

- [PopALM: Popularity-Aligned Language Models for Social Media Trendy Response Prediction](https://mlsanity.com/paper/2402.18950/) (2024)
- [Sentiment-oriented Transformer-based Variational Autoencoder Network for Live Video Commenting](https://mlsanity.com/paper/2404.12782/) (2024)
- [Generating Diversified Comments via Reader-Aware Topic Modeling and Saliency Detection](https://mlsanity.com/paper/2102.06856/) (2021)
- [HotComment: A Benchmark for Evaluating Popularity of Online Comments](https://mlsanity.com/paper/2604.25614/) (2026)
- [Expos'ia: Teaching and Assessment of Academic Writing Skills for Research Project Proposals and Peer Feedback](https://mlsanity.com/paper/2601.06536/) (2026)
- [Towards Controlled and Diverse Generation of Article Comments](https://mlsanity.com/paper/2107.11781/) (2021)
- [You talk what you read: Understanding News Comment Behavior by Dispositional and Situational Attribution](https://mlsanity.com/paper/2308.02168/) (2023)

## Abstract (arXiv)

Comments of online articles provide extended views and improve user engagement. Automatically making comments thus become a valuable functionality for online forums, intelligent chatbots, etc. This paper proposes the new task of automatic article commenting, and introduces a large-scale Chinese dataset with millions of real comments and a human-annotated subset characterizing the comments' varying quality. Incorporating the human bias of comment quality, we further develop automatic metrics that generalize a broad set of popular reference-based metrics and exhibit greatly improved correlations with human evaluations.