@inproceedings{nambanoor-kunnath-etal-2022-dynamic,
    title = "Dynamic Context Extraction for Citation Classification",
    author = "Nambanoor Kunnath, Suchetha  and
      Pride, David  and
      Knoth, Petr",
    editor = "He, Yulan  and
      Ji, Heng  and
      Li, Sujian  and
      Liu, Yang  and
      Chang, Chua-Hui",
    booktitle = "Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = nov,
    year = "2022",
    address = "Online only",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.aacl-main.41/",
    doi = "10.18653/v1/2022.aacl-main.41",
    pages = "539--549",
    abstract = "We investigate the effect of varying citation context window sizes on model performance in citation intent classification. Prior studies have been limited to the application of fixed-size contiguous citation contexts or the use of manually curated citation contexts. We introduce a new automated unsupervised approach for the selection of a dynamic-size and potentially non-contiguous citation context, which utilises the transformer-based document representations and embedding similarities. Our experiments show that the addition of non-contiguous citing sentences improves performance beyond previous results. Evalu- ating on the (1) domain-specific (ACL-ARC) and (2) the multi-disciplinary (SDP-ACT) dataset demonstrates that the inclusion of additional context beyond the citing sentence significantly improves the citation classifi- cation model`s performance, irrespective of the dataset`s domain. We release the datasets and the source code used for the experiments at: \url{https://github.com/oacore/dynamic_citation_context}"
}