# Generating Diversified Comments via Reader-Aware Topic Modeling and Saliency Detection

arXiv: https://arxiv.org/abs/2102.06856 · HTML: https://mlsanity.com/paper/2102.06856/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 29 references that are in the map.
- First posted on arXiv 13 Feb 2021.

## Builds on (most cited first, 21 of 21)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015)
- [Defending Against Neural Fake News](https://mlsanity.com/paper/1905.12616/) (2019)
- [A Persona-Based Neural Conversation Model](https://mlsanity.com/paper/1603.06155/) (2016)
- [Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders](https://mlsanity.com/paper/1703.10960/) (2017)
- [Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering](https://mlsanity.com/paper/1611.05148/) (2016)
- [Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation](https://mlsanity.com/paper/1703.09902/) (2017)
- [The Design and Implementation of XiaoIce, an Empathetic Social Chatbot](https://mlsanity.com/paper/1812.08989/) (2018)
- [The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation](https://mlsanity.com/paper/1804.09849/) (2018)
- [From Eliza to XiaoIce: Challenges and Opportunities with Social Chatbots](https://mlsanity.com/paper/1801.01957/) (2018)
- [Not All Attention Is Needed: Gated Attention Network for Sequence Data](https://mlsanity.com/paper/1912.00349/) (2019)
- [Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation](https://mlsanity.com/paper/1909.11974/) (2019)
- [Automatic Article Commenting: the Task and Dataset](https://mlsanity.com/paper/1805.03668/) (2018)
- [Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model](https://mlsanity.com/paper/1906.01231/) (2019)
- [Generating Pertinent and Diversified Comments with Topic-aware Pointer-Generator Networks](https://mlsanity.com/paper/2005.04396/) (2020)

## Built on by (most cited first, 5 of 5)

- [Let Silence Speak: Enhancing Fake News Detection with Generated Comments from Large Language Models](https://mlsanity.com/paper/2405.16631/) (2024)
- [Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting](https://mlsanity.com/paper/2310.13297/) (2023)
- [PopALM: Popularity-Aligned Language Models for Social Media Trendy Response Prediction](https://mlsanity.com/paper/2402.18950/) (2024)
- [Contrastive Representation Learning for Exemplar-Guided Paraphrase Generation](https://mlsanity.com/paper/2109.01484/) (2021)
- [You talk what you read: Understanding News Comment Behavior by Dispositional and Situational Attribution](https://mlsanity.com/paper/2308.02168/) (2023)

## Abstract (arXiv)

Automatic comment generation is a special and challenging task to verify the model ability on news content comprehension and language generation. Comments not only convey salient and interesting information in news articles, but also imply various and different reader characteristics which we treat as the essential clues for diversity. However, most of the comment generation approaches only focus on saliency information extraction, while the reader-aware factors implied by comments are neglected. To address this issue, we propose a unified reader-aware topic modeling and saliency information detection framework to enhance the quality of generated comments. For reader-aware topic modeling, we design a variational generative clustering algorithm for latent semantic learning and topic mining from reader comments. For saliency information detection, we introduce Bernoulli distribution estimating on news content to select saliency information. The obtained topic representations as well as the selected saliency information are incorporated into the decoder to generate diversified and informative comments. Experimental results on three datasets show that our framework outperforms existing baseline methods in terms of both automatic metrics and human evaluation. The potential ethical issues are also discussed in detail.