@article{chang-etal-2026-dr,
    title = "{DR}-{CUP}: A Dataset on Real-time Commentary in {U}.{S}. Presidential Debates",
    author = "Chang, Yu-Yu  and
      Ho, Huan-Wen  and
      Chen, Chung-Chi  and
      Wang, Ming-Hung",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.772/",
    pages = "9850--9860",
    abstract = "Presidential debates are critical platforms for political discourse, yet existing research lacks datasets tailored for analyzing real-time professional commentary. To address this, we introduce the Dataset on Real-time Commentary in U.S. Presidential debates (DR-CUP), which aligns U.S. presidential debate transcripts (2016{--}2024) with professional commentary and annotations. DR-CUP supports research on commentary understanding, planning, and generation, offering insights into expert analysis and its role in contextualizing complex political discourse. In pilot studies, we evaluated state-of-the-art large language models (LLMs), revealing notable performance differences in understanding expert commentary and planning for generating professional commentary. DR-CUP is the first dataset to incorporate real-time cross-document alignment for debate data, providing a comprehensive resource for advancing research in political communication and computational social science."
}