@inproceedings{dadason-etal-2021-icesum,
    title = "{I}ce{S}um: An {I}celandic Text Summarization Corpus",
    author = {Da{\dh}ason, J{\'o}n  and
      Loftsson, Hrafn  and
      Sigur{\dh}ard{\'o}ttir, Salome  and
      Bj{\"o}rnsson, {\TH}orsteinn},
    editor = "Durmus, Esin  and
      Gupta, Vivek  and
      Liu, Nelson  and
      Peng, Nanyun  and
      Su, Yu",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.naacl-srw.2",
    doi = "10.18653/v1/2021.naacl-srw.2",
    pages = "9--14",
    abstract = "Automatic Text Summarization (ATS) is the task of generating concise and fluent summaries from one or more documents. In this paper, we present IceSum, the first Icelandic corpus annotated with human-generated summaries. IceSum consists of 1,000 online news articles and their extractive summaries. We train and evaluate several neural network-based models on this dataset, comparing them against a selection of baseline methods. We find that an encoder-decoder model with a sequence-to-sequence based extractor obtains the best results, outperforming all baseline methods. Furthermore, we evaluate how the size of the training corpus affects the quality of the generated summaries. We release the corpus and the models with an open license.",
}