  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.
