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        <title>Segmentation of Argumentative Texts by Key Statements for Argument Mining from the Web</title>
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            <title>Proceedings of the 12th Argument mining Workshop</title>
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    <abstract>Argument mining is the task of identifying the argument structure of a text: claims, premises, support/attack relations, etc. However, determining the complete argument structure can be quite involved, especially for unpolished texts from online forums, while for many applications the identification of argumentative key statements would suffice (e.g., for argument search). To this end, we introduce and investigate the new task of segmenting an argumentative text by its key statements. We formalize the task, create a first dataset from online communities, propose an evaluation scheme, and conduct a pilot study with several approaches. Interestingly, our experimental results indicate that none of the tested approaches (even LLM-based ones) can actually satisfactorily solve key statement segmentation yet.</abstract>
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%0 Conference Proceedings
%T Segmentation of Argumentative Texts by Key Statements for Argument Mining from the Web
%A Zelch, Ines
%A Hagen, Matthias
%A Stein, Benno
%A Kiesel, Johannes
%Y Chistova, Elena
%Y Cimiano, Philipp
%Y Haddadan, Shohreh
%Y Lapesa, Gabriella
%Y Ruiz-Dolz, Ramon
%S Proceedings of the 12th Argument mining Workshop
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-258-9
%F zelch-etal-2025-segmentation
%X Argument mining is the task of identifying the argument structure of a text: claims, premises, support/attack relations, etc. However, determining the complete argument structure can be quite involved, especially for unpolished texts from online forums, while for many applications the identification of argumentative key statements would suffice (e.g., for argument search). To this end, we introduce and investigate the new task of segmenting an argumentative text by its key statements. We formalize the task, create a first dataset from online communities, propose an evaluation scheme, and conduct a pilot study with several approaches. Interestingly, our experimental results indicate that none of the tested approaches (even LLM-based ones) can actually satisfactorily solve key statement segmentation yet.
%R 10.18653/v1/2025.argmining-1.22
%U https://aclanthology.org/2025.argmining-1.22/
%U https://doi.org/10.18653/v1/2025.argmining-1.22
%P 228-242

Markdown (Informal)

[Segmentation of Argumentative Texts by Key Statements for Argument Mining from the Web](https://aclanthology.org/2025.argmining-1.22/) (Zelch et al., ArgMining 2025)

• Segmentation of Argumentative Texts by Key Statements for Argument Mining from the Web (Zelch et al., ArgMining 2025)

ACL

• Ines Zelch, Matthias Hagen, Benno Stein, and Johannes Kiesel. 2025. Segmentation of Argumentative Texts by Key Statements for Argument Mining from the Web. In Proceedings of the 12th Argument mining Workshop, pages 228–242, Vienna, Austria. Association for Computational Linguistics.