# Designing and Analysing Argument Mining Pipelines: Towards a Comprehensive Assessment

arXiv: https://arxiv.org/abs/2609.26338 · HTML: https://mlsanity.com/paper/2609.26338/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 69 references that are in the map.
- First posted on arXiv 22 Sep 2026.

## Builds on (most cited first, 19 of 19)

- [Stance Detection on Social Media: State of the Art and Trends](https://mlsanity.com/paper/2006.03644/) (2020)
- [A Large-scale Dataset for Argument Quality Ranking: Construction and Analysis](https://mlsanity.com/paper/1911.11408/) (2019)
- [Neural End-to-End Learning for Computational Argumentation Mining](https://mlsanity.com/paper/1704.06104/) (2017)
- [Argument Mining with Structured SVMs and RNNs](https://mlsanity.com/paper/1704.06869/) (2017)
- [Fine-Grained Argument Unit Recognition and Classification](https://mlsanity.com/paper/1904.09688/) (2019)
- [Transformer-Based Models for Automatic Identification of Argument Relations: A Cross-Domain Evaluation](https://mlsanity.com/paper/2011.13187/) (2020)
- [Classifying Argumentative Relations Using Logical Mechanisms and Argumentation Schemes](https://mlsanity.com/paper/2105.07571/) (2021)
- [Scientia Potentia Est -- On the Role of Knowledge in Computational Argumentation](https://mlsanity.com/paper/2107.00281/) (2021)
- [The evolution of argumentation mining: From models to social media and emerging tools](https://mlsanity.com/paper/1907.02258/) (2019)
- [Towards an Argument Mining Pipeline Transforming Texts to Argument Graphs](https://mlsanity.com/paper/2006.04562/) (2020)
- [VIANA: Visual Interactive Annotation of Argumentation](https://mlsanity.com/paper/1907.12413/) (2019)
- [End-to-End Argument Mining for Discussion Threads Based on Parallel Constrained Pointer Architecture](https://mlsanity.com/paper/1809.00563/) (2018)
- [Understanding and Detecting Supporting Arguments of Diverse Types](https://mlsanity.com/paper/1705.00045/) (2017)
- [Contextual Argument Component Classification for Class Discussions](https://mlsanity.com/paper/2102.10290/) (2021)
- [AntCritic: Argument Mining for Free-Form and Visually-Rich Financial Comments](https://mlsanity.com/paper/2208.09612/) (2022)
- [AutoAM: An End-To-End Neural Model for Automatic and Universal Argument Mining](https://mlsanity.com/paper/2309.09300/) (2023)
- [KNOWCOMP POKEMON Team at DialAM-2024: A Two-Stage Pipeline for Detecting Relations in Dialogical Argument Mining](https://mlsanity.com/paper/2407.19740/) (2024)
- [Mining Arguments from Cancer Documents Using Natural Language Processing and Ontologies](https://mlsanity.com/paper/1607.08074/) (2016)
- [ArguSense: Argument-Centric Analysis of Online Discourse](https://mlsanity.com/paper/2405.15930/) (2024)

## Abstract (arXiv)

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.