Constructing Contrastive samples via Summarization for Text Classification with limited annotations

TL;DR

This paper introduces a novel contrastive learning approach for text classification that uses summarization-based data augmentation and Mixsum regularization to improve performance with limited labeled data.

Contribution

It proposes a new method for constructing contrastive samples via summarization and combines it with Mixsum regularization, advancing contrastive learning in language tasks.

Findings

Improved text classification accuracy on multiple datasets.

Effective contrastive sample construction using summarization.

Enhanced performance with Mixsum regularization.

Abstract

Contrastive Learning has emerged as a powerful representation learning method and facilitates various downstream tasks especially when supervised data is limited. How to construct efficient contrastive samples through data augmentation is key to its success. Unlike vision tasks, the data augmentation method for contrastive learning has not been investigated sufficiently in language tasks. In this paper, we propose a novel approach to construct contrastive samples for language tasks using text summarization. We use these samples for supervised contrastive learning to gain better text representations which greatly benefit text classification tasks with limited annotations. To further improve the method, we mix up samples from different classes and add an extra regularization, named Mixsum, in addition to the cross-entropy-loss. Experiments on real-world text classification datasets (Amazon-5, Yelp-5, AG News, and IMDb) demonstrate the effectiveness of the proposed contrastive learning framework with summarization-based data augmentation and Mixsum regularization.