CU-MAM: Coherence-Driven Unified Macro-Structures for Argument Mining (2025.acl-long)
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| Challenge: | Argument Mining (AM) involves the automatic identification of argument structure in natural language. |
| Approach: | They propose an approach that captures local and global coherence to identify argument structures by modeling macro-structure. |
| Outcome: | The proposed approach shows superior performance on heterogeneous datasets and on unseen datasets. |
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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. |
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| Challenge: | Argument mining involves the identification of argument relations (AR) between Argumentative Discourse Units (ADUs). |
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AM4DSP: Argumentation Mining in Structured Decentralized Discussion Platforms for Deliberative Democracy (2025.emnlp-demos)
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| Challenge: | Argument mining is the automated process of identification and extraction of argumentative structures in natural language. |
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Argument Mining with Fine-Tuned Large Language Models (2025.coling-main)
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| Challenge: | Argument Mining (AM) pipelines use fine-tuned large language models (LLMs) . initial approaches employ supervised machine learning algorithms, such as Maximum Entropy classifiers and Logistic Regressions. |
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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. |
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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. |
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End-to-end Argument Mining with Cross-corpora Multi-task Learning (2022.tacl-1)
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| Challenge: | Argument(ation) mining is a task of identifying argument structure from text . lack of training data makes it difficult to train models based on limited data sets. |
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| Challenge: | Recent advances in AM models overlook the integration of supplementary discourse structure information, resulting in suboptimal outcomes. |
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| Challenge: | Earnings Conference Calls (ECCs) are a favoured domain for the study of argumentation in context and the extraction of Argumentative Discourse Units (ADUs). |
| Approach: | Earnings Conference Calls (ECCs) are favoured domain for study of argumentation in context and extraction of Argumentative Discourse Units (ADUs). |
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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). |
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