Towards the TopMost: A Topic Modeling System Toolkit
This toolkit addresses the problem of hindered research progress and applications in topic modeling for researchers and practitioners, but it is incremental as it builds upon existing toolkits with broader coverage.
The authors tackled the challenge of disparate datasets, implementations, and evaluations in topic modeling by proposing TopMost, a toolkit that supports extensive features and enables rapid utilization, fair comparisons, and flexible extensions of diverse topic models.
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.