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        <title>CU-MAM: Coherence-Driven Unified Macro-Structures for Argument Mining</title>
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    <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 (**local coherence**) and across segments (**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.</abstract>
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%0 Conference Proceedings
%T CU-MAM: Coherence-Driven Unified Macro-Structures for Argument Mining
%A Gemechu, Debela
%A Reed, Chris
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F gemechu-reed-2025-cu
%X 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 (**local coherence**) and across segments (**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.
%R 10.18653/v1/2025.acl-long.969
%U https://aclanthology.org/2025.acl-long.969/
%U https://doi.org/10.18653/v1/2025.acl-long.969
%P 19731-19749

Markdown (Informal)

[CU-MAM: Coherence-Driven Unified Macro-Structures for Argument Mining](https://aclanthology.org/2025.acl-long.969/) (Gemechu & Reed, ACL 2025)

• CU-MAM: Coherence-Driven Unified Macro-Structures for Argument Mining (Gemechu & Reed, ACL 2025)

ACL

• Debela Gemechu and Chris Reed. 2025. CU-MAM: Coherence-Driven Unified Macro-Structures for Argument Mining. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 19731–19749, Vienna, Austria. Association for Computational Linguistics.