Clayton, J., Damonte, M. and Gaizauskas, R. orcid.org/0000-0002-3356-5126 (2024) Parsing graphical summaries from argumentative dialogues. In: Reed, C., Thimm, M. and Rienstra, T., (eds.) Computational Models of Argument: Proceedings of COMMA 2024. The 10th International Conference on Computational Models of Argument, 18-20 Sep 2024, Hagen, Germany. Frontiers in Artificial Intelligence and Applications, 388 . IOS Press , pp. 37-48. ISBN 9781643685342
Abstract
In this paper, we introduce a novel Argument Mining task based on the existing task of Argument Structure Parsing (ASP). Our new task, which we call ASG Parsing, is the task of generating Argument Summary Graphs (ASGs) from dialogical argumentative text. We release a dataset containing ASGs, a type of graphical summary for argumentative dialogues, in which the nodes are summaries of statements and the edges are the argumentative relations between them (support or attack). We approach the problem with two different LLM-based solutions: (a) a pipeline system involving two models separately fine-tuned for summarisation and stance detection; and (b) an end-to-end system based on the TANL (Translation between Augmented Natural Languages) framework [1]. We show that the TANL approach outperforms the pipeline approach across the board. We also show that, for all systems, performance degrades as the depth of the graphs increases.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2024 The Authors. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0 / https://creativecommons.org/licenses/by-nc/4.0/) |
Keywords: | Argument Mining; Summarisation; Stance Detection; TANL |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/S023062/1 COMMERCIAL MASTER ACCOUNT UNSPECIFIED |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 18 Feb 2025 15:23 |
Last Modified: | 18 Feb 2025 15:24 |
Status: | Published |
Publisher: | IOS Press |
Series Name: | Frontiers in Artificial Intelligence and Applications |
Refereed: | Yes |
Identification Number: | 10.3233/FAIA240308 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:223504 |