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    <abstract>We create, and make publicly available, a novel dataset for the task of Argument Summary Graph Parsing (ASGP), which we call SENSEI-ASG, based on annotating a subset of the SENSEI corpus. Given an argumentative dialogue, such as might be found in a social media exchange, ASGP is the task of creating an Argument Summary Graph, a data structure which consists of nodes containing summaries of arguments in a dialogue, and edges showing argumentative relations between them. We find that the only existing ASG dataset, Debatabase-ASG, is not representative of online debates in language use, length of the dialogues, or graph complexity. In contrast to Debatabase-ASG, which was created based on a curated debate collection, SENSEI-ASG contains examples of spontaneous debates arising in the comments sections of an online newspaper (namely, The Guardian). We achieve moderate inter-annotator agreement on the dataset, with a Cohen’s kappa of k=0.57, reflecting the inherent challenges in distinguishing argumentative from non-argumentative text. We propose baselines for the new dataset by fine-tuning Llama-3 for the ASGP task, using the two ASGP datasets and an additional out-of-domain argument mining dataset, the AAEC.</abstract>
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%0 Conference Proceedings
%T SENSEI-ASG: A Challenging Dataset for Argument Summary Graph Parsing
%A Clayton, Jonathan
%A Damonte, Marco
%A Gaizauskas, Robert
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F clayton-etal-2026-sensei
%X We create, and make publicly available, a novel dataset for the task of Argument Summary Graph Parsing (ASGP), which we call SENSEI-ASG, based on annotating a subset of the SENSEI corpus. Given an argumentative dialogue, such as might be found in a social media exchange, ASGP is the task of creating an Argument Summary Graph, a data structure which consists of nodes containing summaries of arguments in a dialogue, and edges showing argumentative relations between them. We find that the only existing ASG dataset, Debatabase-ASG, is not representative of online debates in language use, length of the dialogues, or graph complexity. In contrast to Debatabase-ASG, which was created based on a curated debate collection, SENSEI-ASG contains examples of spontaneous debates arising in the comments sections of an online newspaper (namely, The Guardian). We achieve moderate inter-annotator agreement on the dataset, with a Cohen’s kappa of k=0.57, reflecting the inherent challenges in distinguishing argumentative from non-argumentative text. We propose baselines for the new dataset by fine-tuning Llama-3 for the ASGP task, using the two ASGP datasets and an additional out-of-domain argument mining dataset, the AAEC.
%R 10.63317/3abueoaae2s2
%U https://aclanthology.org/2026.lrec-1.648/
%U https://doi.org/10.63317/3abueoaae2s2
%P 8174-8189

Markdown (Informal)

[SENSEI-ASG: A Challenging Dataset for Argument Summary Graph Parsing](https://aclanthology.org/2026.lrec-1.648/) (Clayton et al., LREC 2026)

• SENSEI-ASG: A Challenging Dataset for Argument Summary Graph Parsing (Clayton et al., LREC 2026)

ACL

• Jonathan Clayton, Marco Damonte, and Robert Gaizauskas. 2026. SENSEI-ASG: A Challenging Dataset for Argument Summary Graph Parsing. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 8174–8189, Palma de Mallorca, Spain. ELRA Language Resource Association.