# Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation

arXiv: https://arxiv.org/abs/1909.11974 · HTML: https://mlsanity.com/paper/1909.11974/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2019.

## Built on by (most cited first, 12 of 12)

- [N24News: A New Dataset for Multimodal News Classification](https://mlsanity.com/paper/2108.13327/) (2021)
- [Unsupervised and Distributional Detection of Machine-Generated Text](https://mlsanity.com/paper/2111.02878/) (2021)
- [Let Silence Speak: Enhancing Fake News Detection with Generated Comments from Large Language Models](https://mlsanity.com/paper/2405.16631/) (2024)
- [SciNews: From Scholarly Complexities to Public Narratives -- A Dataset for Scientific News Report Generation](https://mlsanity.com/paper/2403.17768/) (2024)
- [Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting](https://mlsanity.com/paper/2310.13297/) (2023)
- [Uncovering the Limits of Text-based Emotion Detection](https://mlsanity.com/paper/2109.01900/) (2021)
- [Measuring the Effect of Influential Messages on Varying Personas](https://mlsanity.com/paper/2305.16470/) (2023)
- [PopALM: Popularity-Aligned Language Models for Social Media Trendy Response Prediction](https://mlsanity.com/paper/2402.18950/) (2024)
- [Generating Diversified Comments via Reader-Aware Topic Modeling and Saliency Detection](https://mlsanity.com/paper/2102.06856/) (2021)
- [Private Language Models via Truncated Laplacian Mechanism](https://mlsanity.com/paper/2410.08027/) (2024)
- [Evaluating Ensemble Methods for News Recommender Systems](https://mlsanity.com/paper/2406.16106/) (2024)
- [DIPBox: A Multi-scale Testing Framework for Tracking Dataset Regeneration](https://mlsanity.com/paper/2606.21240/) (2026)

## Abstract (arXiv)

Automatic news comment generation is a new testbed for techniques of natural language generation. In this paper, we propose a "read-attend-comment" procedure for news comment generation and formalize the procedure with a reading network and a generation network. The reading network comprehends a news article and distills some important points from it, then the generation network creates a comment by attending to the extracted discrete points and the news title. We optimize the model in an end-to-end manner by maximizing a variational lower bound of the true objective using the back-propagation algorithm. Experimental results on two datasets indicate that our model can significantly outperform existing methods in terms of both automatic evaluation and human judgment.