From Documents to Segments: A Contextual Reformulation for Topic Assignment

摘要

Traditional topic modeling treats each document as a single, coherent unit of topic, which can cause topic contamination when documents cover multiple topics. This becomes especially problematic when stakeholders are interested in identifying documents that focus on a specific topic. We introduce segment-based topic allocation, a novel paradigm that redefines topic assignment at the level of segments, coherent textual spans conveying distinct topical content. This granularity improves topic purity, interpretability, and applicability to multi-theme corpora such as reviews or survey responses. To support this paradigm, we construct SemEval-STM, a benchmark derived from aspect-based sentiment datasets, where segments are automatically extracted using large language models (LLMs) and post-processed with human supervision. We further propose the segment intrusion task (SIT), a novel evaluation method extending word intrusion to the span level, enabling human-centric assessment of topical coherence. Empirical results across diverse metrics and models demonstrate that SBTA significantly outperforms traditional document-based methods in clustering and interpretability. Our framework provides a practical and scalable solution for fine-grained topic analysis in heterogeneous text corpora.