Topic Modelling Meets Deep Neural Networks: A Survey

TL;DR

This survey reviews the development and applications of neural topic models, highlighting their integration with deep neural networks for advanced text analysis and language understanding.

Contribution

It provides the first comprehensive overview of neural topic models, summarizing research progress, open problems, and future directions in this emerging field.

Findings

Over a hundred neural topic models developed

Applications in text generation, summarisation, and language models

Identification of open challenges and future research directions

Abstract

Topic modelling has been a successful technique for text analysis for almost twenty years. When topic modelling met deep neural networks, there emerged a new and increasingly popular research area, neural topic models, with over a hundred models developed and a wide range of applications in neural language understanding such as text generation, summarisation and language models. There is a need to summarise research developments and discuss open problems and future directions. In this paper, we provide a focused yet comprehensive overview of neural topic models for interested researchers in the AI community, so as to facilitate them to navigate and innovate in this fast-growing research area. To the best of our knowledge, ours is the first review focusing on this specific topic.