@inproceedings{das-etal-2025-role,
title = "On the Role of Key Phrases in Argument Mining",
author = "Das, Nilmadhab and
Saradhi, Vijaya V and
Anand, Ashish",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.319/",
doi = "10.18653/v1/2025.findings-naacl.319",
pages = "5759--5772",
ISBN = "979-8-89176-195-7",
abstract = "Argument mining (AM) focuses on analyzing argumentative structures such as Argument Components (ACs) and Argumentative Relations (ARs). Modeling dependencies between ACs and ARs is challenging due to the complex interactions between ACs. Existing approaches often overlook crucial conceptual links, such as key phrases that connect two related ACs, and tend to rely on cartesian product methods to model these dependencies, which can result in class imbalances. To extract key phrases from the AM benchmarks, we employ a prompt-based strategy utilizing an open-source Large Language Model (LLM). Building on this, we propose a unified text-to-text generation framework that leverages Augmented Natural Language (ANL) formatting and integrates the extracted key phrases inside the ANL itself to efficiently solve multiple AM tasks in a joint formulation. Our method sets new State-of-the-Art (SoTA) on three structurally distinct standard AM benchmarks, surpassing baselines by up to 9.5{\%} F1 score, demonstrating its strong potential."
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<abstract>Argument mining (AM) focuses on analyzing argumentative structures such as Argument Components (ACs) and Argumentative Relations (ARs). Modeling dependencies between ACs and ARs is challenging due to the complex interactions between ACs. Existing approaches often overlook crucial conceptual links, such as key phrases that connect two related ACs, and tend to rely on cartesian product methods to model these dependencies, which can result in class imbalances. To extract key phrases from the AM benchmarks, we employ a prompt-based strategy utilizing an open-source Large Language Model (LLM). Building on this, we propose a unified text-to-text generation framework that leverages Augmented Natural Language (ANL) formatting and integrates the extracted key phrases inside the ANL itself to efficiently solve multiple AM tasks in a joint formulation. Our method sets new State-of-the-Art (SoTA) on three structurally distinct standard AM benchmarks, surpassing baselines by up to 9.5% F1 score, demonstrating its strong potential.</abstract>
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%0 Conference Proceedings
%T On the Role of Key Phrases in Argument Mining
%A Das, Nilmadhab
%A Saradhi, Vijaya V.
%A Anand, Ashish
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F das-etal-2025-role
%X Argument mining (AM) focuses on analyzing argumentative structures such as Argument Components (ACs) and Argumentative Relations (ARs). Modeling dependencies between ACs and ARs is challenging due to the complex interactions between ACs. Existing approaches often overlook crucial conceptual links, such as key phrases that connect two related ACs, and tend to rely on cartesian product methods to model these dependencies, which can result in class imbalances. To extract key phrases from the AM benchmarks, we employ a prompt-based strategy utilizing an open-source Large Language Model (LLM). Building on this, we propose a unified text-to-text generation framework that leverages Augmented Natural Language (ANL) formatting and integrates the extracted key phrases inside the ANL itself to efficiently solve multiple AM tasks in a joint formulation. Our method sets new State-of-the-Art (SoTA) on three structurally distinct standard AM benchmarks, surpassing baselines by up to 9.5% F1 score, demonstrating its strong potential.
%R 10.18653/v1/2025.findings-naacl.319
%U https://aclanthology.org/2025.findings-naacl.319/
%U https://doi.org/10.18653/v1/2025.findings-naacl.319
%P 5759-5772
Markdown (Informal)
[On the Role of Key Phrases in Argument Mining](https://aclanthology.org/2025.findings-naacl.319/) (Das et al., Findings 2025)
ACL
- Nilmadhab Das, Vijaya V Saradhi, and Ashish Anand. 2025. On the Role of Key Phrases in Argument Mining. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 5759–5772, Albuquerque, New Mexico. Association for Computational Linguistics.