Towards the TopMost: A Topic Modeling System Toolkit

TL;DR

This paper introduces TopMost, a comprehensive toolkit for topic modeling that supports various models, datasets, and evaluation methods, facilitating easier use, comparison, and extension of topic modeling techniques.

Contribution

The paper presents TopMost, a modular and extensive toolkit that covers the full lifecycle of topic modeling, improving usability and comparability over existing solutions.

Findings

Supports a wide range of topic models and datasets

Enables rapid and fair comparisons of models

Facilitates flexible extensions and applications

Abstract

Topic models have a rich history with various applications and have recently been reinvigorated by neural topic modeling. However, these numerous topic models adopt totally distinct datasets, implementations, and evaluations. This impedes quick utilization and fair comparisons, and thereby hinders their research progress and applications. To tackle this challenge, we in this paper propose a Topic Modeling System Toolkit (TopMost). Compared to existing toolkits, TopMost stands out by supporting more extensive features. It covers a broader spectrum of topic modeling scenarios with their complete lifecycles, including datasets, preprocessing, models, training, and evaluations. Thanks to its highly cohesive and decoupled modular design, TopMost enables rapid utilization, fair comparisons, and flexible extensions of diverse cutting-edge topic models. Our code, tutorials, and documentation are available at https://github.com/bobxwu/topmost.