Stick-Breaking Embedded Topic Model with Continuous Optimal Transport for Online Analysis of Document Streams

• URL: http://arxiv.org/abs/2510.18786v1
• Date: Tue, 21 Oct 2025 16:40:14 GMT
• Title: Stick-Breaking Embedded Topic Model with Continuous Optimal Transport for Online Analysis of Document Streams
• Authors: Federica Granese, Serena Villata, Charles Bouveyron,
• Abstract summary: SB-SETM is an innovative model extending the Embedded Topic Model (ETM) to process data streams by merging models formed on successive partial document batches.<n> Numerical experiments show SB-SETM outperforming baselines on simulated scenarios.<n>We extensively test it on a real-world corpus of news articles covering the Russian-Ukrainian war throughout 2022-2023.
• Score: 9.41487883751588
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Online topic models are unsupervised algorithms to identify latent topics in data streams that continuously evolve over time. Although these methods naturally align with real-world scenarios, they have received considerably less attention from the community compared to their offline counterparts, due to specific additional challenges. To tackle these issues, we present SB-SETM, an innovative model extending the Embedded Topic Model (ETM) to process data streams by merging models formed on successive partial document batches. To this end, SB-SETM (i) leverages a truncated stick-breaking construction for the topic-per-document distribution, enabling the model to automatically infer from the data the appropriate number of active topics at each timestep; and (ii) introduces a merging strategy for topic embeddings based on a continuous formulation of optimal transport adapted to the high dimensionality of the latent topic space. Numerical experiments show SB-SETM outperforming baselines on simulated scenarios. We extensively test it on a real-world corpus of news articles covering the Russian-Ukrainian war throughout 2022-2023.

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