| Challenge: | Argumentation mining (AM) aims to detect arguments and their inherent relations from textual compositions. |
| Approach: | They propose a method to model the inter-relationships among three subtasks within a generative framework. |
| Outcome: | The proposed method achieves state-of-the-art performance on two AM benchmarks. |
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Learning First-Order Logic Rules for Argumentation Mining (2025.acl-long)
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Yang Sun, Guanrong Chen, Hamid Alinejad-Rokny, Jianzhu Bao, Yuqi Huang, Bin Liang, Kam-Fai Wong, Min Yang, Ruifeng Xu
| Challenge: | Argumentation Mining (AM) aims to extract argumentative structures from texts by identifying argumentation components (ACs) and their argumentative relations (ARs). |
| Approach: | They propose a First- Order Logic reasoning framework for AM to capture logical reasoning paths within argumentative texts. |
| Outcome: | The proposed framework outperforms strong baselines while significantly improving explainability. |
On the Role of Key Phrases in Argument Mining (2025.findings-naacl)
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| Challenge: | Existing approaches to argument mining often overlook crucial conceptual links between ACs and ARs. |
| Approach: | They propose a framework that extracts key phrases from AM benchmarks using an open-source Large Language Model. |
| Outcome: | The proposed framework surpasses baselines on three structurally distinct AM benchmarks by up to 9.5% F1 score, demonstrating its strong potential. |
External Knowledge-Driven Argument Mining: Leveraging Attention-Enhanced Multi-Network Models (2024.emnlp-main)
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| Challenge: | Argument mining involves the identification of argument relations (AR) between Argumentative Discourse Units (ADUs). |
| Approach: | They propose to leverage external resources to identify semantic paths linking ADUs . they propose to use WordNet, ConceptNet, and Wikipedia to identify these paths . |
| Outcome: | The proposed architecture achieves F-scores of 0.85, 0.84, 0.70, and 0.87 on four datasets. |
Towards Better Non-Tree Argument Mining: Proposition-Level Biaffine Parsing with Task-Specific Parameterization (2020.acl-main)
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| Challenge: | Argument mining studies have advanced the ability to predict argument structures, but the technology for capturing non-tree-structured arguments is still in its infancy. |
| Approach: | They propose a neural model that can predict proposition types and edges between propositions. |
| Outcome: | The proposed model improves edge prediction performance compared to baseline models. |
Argument Component Segmentation with Fine-Tuned Large Language Models (2026.findings-eacl)
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| Challenge: | Argument Mining (AM) aims to identify and interpret argumentative structures in unstructured text. |
| Approach: | They propose a fine-grained, paired-tag annotation schema that distinguishes between relevant and surrounding content. |
| Outcome: | The proposed approach performs comparable to human expert annotators across multiple benchmark datasets. |
Argument Mining as a Text-to-Text Generation Task (2024.eacl-long)
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| Challenge: | Argument Mining (AM) aims to uncover the argumentative structures within a text. |
| Approach: | They propose a method that generates argumentatively annotated text using a pretrained encoder-decoder language model and a pre-trained decoder. |
| Outcome: | The proposed method achieves state-of-the-art performance on three types of benchmark datasets. |
Argument mining as a multi-hop generative machine reading comprehension task (2023.findings-emnlp)
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| Challenge: | Argument mining is a natural language processing task that aims to generate an argumentative graph given an unstructured argumentative text. |
| Approach: | They propose a new approach which transfers the argument mining task into a multi-hop reading comprehension task by incorporating a "chain of thought" information into the model. |
| Outcome: | The proposed approach surpasses SOTA results on two arguments mining benchmarks. |
A Generative Model for End-to-End Argument Mining with Reconstructed Positional Encoding and Constrained Pointer Mechanism (2022.emnlp-main)
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| Challenge: | Argument mining (AM) is a challenging task as it requires recognizing complex argumentation structures involving multiple subtasks. |
| Approach: | They propose a generative framework where expected outputs of AM are framed as a simple target sequence. |
| Outcome: | The proposed framework achieves state-of-the-art on two AM benchmarks. |
A Neural Transition-based Model for Argumentation Mining (2021.acl-long)
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| Challenge: | Existing methods for identifying argumentation structures are inefficient and class imbalanced. |
| Approach: | They propose a neural transition-based model that incrementally builds an argumentation graph by generating a sequence of actions. |
| Outcome: | The proposed model can handle tree and non-tree structured argumentation without structural constraints. |
AQE: Argument Quadruplet Extraction via a Quad-Tagging Augmented Generative Approach (2023.findings-acl)
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| Challenge: | Argument mining involves multiple subtasks, but each one is insufficient for understanding argumentative structure and reasoning process. |
| Approach: | They propose a quadruplet extraction task that extracts four argumentative components . they use a generative quadragging module to augment the training of the generative framework . |
| Outcome: | The proposed method can extract arguments from a large-scale dataset. |