Designing and Analysing Argument Mining Pipelines: Towards a Comprehensive Assessment

2026-09-22

AI Summary

本文针对论证挖掘（AM）任务间难以直接比较的问题，提出了一种三视角框架来分析和设计AM流程，以促进更清晰的对比和评估。

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.

Problem

• Argument Mining
• task-level analysis
• end-to-end pipeline
• comparative assessment
• meta-study

Innovation

• Argument Mining
• Triple-Perspective Framework
• Systematic Comparison
• General Design
• Methodology-Centered Descriptions