CATCH: A Controllable Theme Detection Framework with Contextualized Clustering and Hierarchical Generation

• URL: http://arxiv.org/abs/2512.21715v1
• Date: Thu, 25 Dec 2025 15:33:25 GMT
• Title: CATCH: A Controllable Theme Detection Framework with Contextualized Clustering and Hierarchical Generation
• Authors: Rui Ke, Jiahui Xu, Shenghao Yang, Kuang Wang, Feng Jiang, Haizhou Li,
• Abstract summary: We propose a unified framework that integrates three core components: context-aware topic representation, preference-guided topic clustering, and a hierarchical theme generation mechanism.<n>Experiments on a multi-domain customer dialogue benchmark (DSTC-12) demonstrate the effectiveness of CATCH with 8B LLM in both theme clustering and topic generation quality.
• Score: 33.065240934374586
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Theme detection is a fundamental task in user-centric dialogue systems, aiming to identify the latent topic of each utterance without relying on predefined schemas. Unlike intent induction, which operates within fixed label spaces, theme detection requires cross-dialogue consistency and alignment with personalized user preferences, posing significant challenges. Existing methods often struggle with sparse, short utterances for accurate topic representation and fail to capture user-level thematic preferences across dialogues. To address these challenges, we propose CATCH (Controllable Theme Detection with Contextualized Clustering and Hierarchical Generation), a unified framework that integrates three core components: (1) context-aware topic representation, which enriches utterance-level semantics using surrounding topic segments; (2) preference-guided topic clustering, which jointly models semantic proximity and personalized feedback to align themes across dialogue; and (3) a hierarchical theme generation mechanism designed to suppress noise and produce robust, coherent topic labels. Experiments on a multi-domain customer dialogue benchmark (DSTC-12) demonstrate the effectiveness of CATCH with 8B LLM in both theme clustering and topic generation quality.

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