@inproceedings{ivanova-etal-2024-lets,
    title = "Let`s discuss! Quality Dimensions and Annotated Datasets for Computational Argument Quality Assessment",
    author = "Ivanova, Rositsa V  and
      Huber, Thomas  and
      Niklaus, Christina",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.emnlp-main.1155/",
    doi = "10.18653/v1/2024.emnlp-main.1155",
    pages = "20749--20779",
    abstract = "Research in the computational assessment of Argumentation Quality has gained popularity over the last ten years. Various quality dimensions have been explored through the creation of domain-specific datasets and assessment methods. We survey the related literature (211 publications and 32 datasets), while addressing potential overlaps and blurry boundaries to related domains. This paper provides a representative overview of the state of the art in Computational Argument Quality Assessment with a focus on quality dimensions and annotated datasets. The aim of the survey is to identify research gaps and to aid future discussions and work in the domain."
}