@inproceedings{gemechu-reed-2025-cu,
    title = "{CU}-{MAM}: Coherence-Driven Unified Macro-Structures for Argument Mining",
    author = "Gemechu, Debela  and
      Reed, Chris",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/cawl-year/2025.acl-long.969/",
    doi = "10.18653/v1/2025.acl-long.969",
    pages = "19731--19749",
    ISBN = "979-8-89176-251-0",
    abstract = "Argument Mining (AM) involves the automatic identification of argument structure in natural language. Traditional AM methods rely on micro-structural features derived from the internal properties of individual Argumentative Discourse Units (ADUs). However, argument structure is shaped by a macro-structure capturing the functional interdependence among ADUs. This macro-structure consists of segments, where each segment contains ADUs that fulfill specific roles to maintain coherence within the segment (\textbf{local coherence}) and across segments (\textbf{global coherence}). This paper presents an approach that models macro-structure, capturing both local and global coherence to identify argument structures. Experiments on heterogeneous datasets demonstrate superior performance in both in-dataset and cross-dataset evaluations. The cross-dataset evaluation shows that macro-structure enhances transferability to unseen datasets."
}