@inproceedings{yoon-etal-2026-documents,
    title = "From Documents to Segments: A Contextual Reformulation for Topic Assignment",
    author = "Yoon, Hoonsang  and
      Kim, Takyoung  and
      Lee, Wonkee  and
      Cho, Ilmin  and
      Hakkani-T{\textbackslash}{''}ur, Dilek  and
      Choi, Stanley Jungkyu",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1278/",
    pages = "25586--25624",
    ISBN = "979-8-89176-395-1",
    abstract = "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."
}