@inproceedings{clayton-etal-2026-sensei,
    title = "{SENSEI}-{ASG}: A Challenging Dataset for Argument Summary Graph Parsing",
    author = "Clayton, Jonathan  and
      Damonte, Marco  and
      Gaizauskas, Robert",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/paragraph-normalization/2026.lrec-1.648/",
    doi = "10.63317/3abueoaae2s2",
    pages = "8174--8189",
    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."
}