# Attentive Representation Learning with Adversarial Training for Short Text Clustering

arXiv: https://arxiv.org/abs/1912.03720 · HTML: https://mlsanity.com/paper/1912.03720/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2019.

## Built on by (most cited first, 2 of 2)

- [Short Text Clustering with Transformers](https://mlsanity.com/paper/2102.00541/) (2021)
- [An Enhanced Model-based Approach for Short Text Clustering](https://mlsanity.com/paper/2507.13793/) (2025)

## Abstract (arXiv)

Short text clustering has far-reaching effects on semantic analysis, showing its importance for multiple applications such as corpus summarization and information retrieval. However, it inevitably encounters the severe sparsity of short text representations, making the previous clustering approaches still far from satisfactory. In this paper, we present a novel attentive representation learning model for shot text clustering, wherein cluster-level attention is proposed to capture the correlations between text representations and cluster representations. Relying on this, the representation learning and clustering for short texts are seamlessly integrated into a unified model. To further ensure robust model training for short texts, we apply adversarial training to the unsupervised clustering setting, by injecting perturbations into the cluster representations. The model parameters and perturbations are optimized alternately through a minimax game. Extensive experiments on four real-world short text datasets demonstrate the superiority of the proposed model over several strong competitors, verifying that robust adversarial training yields substantial performance gains.