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.

翻译：主题模型拥有丰富的历史背景和多样化的应用，近年来因神经主题建模而重新焕发活力。然而，这些众多的主题模型采用了完全不同的数据集、实现方式和评估标准。这阻碍了其快速应用与公平比较，从而限制了研究进展与实际应用。为应对这一挑战，本文提出了一种主题建模系统工具包（TopMost）。与现有工具包相比，TopMost在支持更广泛功能方面表现突出。它涵盖了更全面的主题建模场景及其完整生命周期，包括数据集、预处理、模型、训练和评估。得益于其高内聚、低耦合的模块化设计，TopMost能够实现对各类前沿主题模型的快速应用、公平比较和灵活扩展。我们的代码、教程和文档可在 https://github.com/bobxwu/topmost 获取。