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arXiv · 2609.26338

Designing and Analysing Argument Mining Pipelines: Towards a Comprehensive Assessment

Siddharth Bhargava·Sara Tonelli·Patricia Martín-Rodilla
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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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Siddharth Bhargava, Sara Tonelli, Patricia Martín-Rodilla. 2026-09-22. Designing and Analysing Argument Mining Pipelines: Towards a Comprehensive Assessment. https://arxiv.org/abs/2609.26338

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