Text Classification using Graph Convolutional Networks: A Comprehensive Survey

TL;DR

This survey reviews recent graph convolutional network approaches for text classification, categorizing architectures, comparing performance, and discussing future challenges and research directions in this rapidly evolving field.

Contribution

It provides a comprehensive categorization and comparison of GCN-based text classification methods, highlighting their strengths, limitations, and future research avenues.

Findings

GCN approaches achieve state-of-the-art performance on benchmark datasets

Different GCN architectures vary in effectiveness and computational efficiency

The survey identifies key challenges and future directions in GCN-based text classification

Abstract

Text classification is a quintessential and practical problem in natural language processing with applications in diverse domains such as sentiment analysis, fake news detection, medical diagnosis, and document classification. A sizable body of recent works exists where researchers have studied and tackled text classification from different angles with varying degrees of success. Graph convolution network (GCN)-based approaches have gained a lot of traction in this domain over the last decade with many implementations achieving state-of-the-art performance in more recent literature and thus, warranting the need for an updated survey. This work aims to summarize and categorize various GCN-based Text Classification approaches with regard to the architecture and mode of supervision. It identifies their strengths and limitations and compares their performance on various benchmark datasets. We also discuss future research directions and the challenges that exist in this domain.