@inproceedings{weber-etal-2023-structured,
    title = "Structured Persuasive Writing Support in Legal Education: A Model and Tool for {G}erman Legal Case Solutions",
    author = "Weber, Florian  and
      Wambsganss, Thiemo  and
      Neshaei, Seyed Parsa  and
      Soellner, Matthias",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.145",
    doi = "10.18653/v1/2023.findings-acl.145",
    pages = "2296--2313",
    abstract = "We present an annotation approach for capturing structured components and arguments inlegal case solutions of German students. Based on the appraisal style, which dictates the structured way of persuasive writing in German law, we propose an annotation scheme with annotation guidelines that identify structured writing in legal case solutions. We conducted an annotation study with two annotators and annotated legal case solutions to capture the structures of a persuasive legal text. Based on our dataset, we trained three transformer-based models to show that the annotated components can be successfully predicted, e.g. to provide users with writing assistance for legal texts. We evaluated a writing support system in which our models were integrated in an online experiment with law students and found positive learning success and users{'} perceptions. Finally, we present our freely available corpus of 413 law student case studies to support the development of intelligent writing support systems.",
}

We present an annotation approach for capturing structured components and arguments inlegal case solutions of German students. Based on the appraisal style, which dictates the structured way of persuasive writing in German law, we propose an annotation scheme with annotation guidelines that identify structured writing in legal case solutions. We conducted an annotation study with two annotators and annotated legal case solutions to capture the structures of a persuasive legal text. Based on our dataset, we trained three transformer-based models to show that the annotated components can be successfully predicted, e.g. to provide users with writing assistance for legal texts. We evaluated a writing support system in which our models were integrated in an online experiment with law students and found positive learning success and users{'} perceptions. Finally, we present our freely available corpus of 413 law student case studies to support the development of intelligent writing support systems.
Weber, Florian Wambsganss, Thiemo Neshaei, Seyed Parsa Soellner, Matthias
Structured Persuasive Writing Support in Legal Education: A Model and Tool for German Legal Case Solutions