Designing and Analysing Argument Mining Pipelines: Towards a Comprehensive Assessment

arXiv:2609.26338 · cs.CL · Submitted 2026-09-22 · Read on arXiv

cs.CL

Submitted: 2026-09-22

Updated: 2026-09-22

Comments: 12 pages, 3 figures, European Conference on Argumentation 2025 (ECA 2025)

Code: https://github.com/The-obsrvr/ArgumentMiningPipelines

License: http://creativecommons.org/licenses/by/4.0/

The gist: Argument Mining (AM) transforms natural language into its underlying argument structures.

Terminology

Abstract

Argument Mining (AM) transforms natural language into its underlying argument structures. This transformation is typically realized through a sequence of AM tasks that form an end-to-end AM pipeline. However, AM approaches often differ in how they conceptualize these tasks, making direct comparisons between them difficult and opaque. This calls for a more nuanced, task-level analysis of AM approaches to enable clearer comparison and assessment. This work presents a preliminary meta-study that systematically reviews several state-of-the-art end-to-end AM works and analyzes their pipelines through a triple-perspective framework---a linguistic, computational and domain perspective---to understand how the pipelines model arguments as structures, computes them, and integrates domain knowledge. We further propose a general design to the linguistic and computational perspectives, illustrating how key AM tasks are designed for modeling and computation of argument structures. Our proposed framework lays the groundwork for methodology-centered descriptions across AM approaches, facilitating deeper understanding and more systematic comparisons in future research.

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