@inproceedings{alabdulkarim-etal-2021-automatic,
    title = "Automatic Story Generation: Challenges and Attempts",
    author = "Alabdulkarim, Amal  and
      Li, Siyan  and
      Peng, Xiangyu",
    editor = "Akoury, Nader  and
      Brahman, Faeze  and
      Chaturvedi, Snigdha  and
      Clark, Elizabeth  and
      Iyyer, Mohit  and
      Martin, Lara J.",
    booktitle = "Proceedings of the Third Workshop on Narrative Understanding",
    month = jun,
    year = "2021",
    address = "Virtual",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.nuse-1.8/",
    doi = "10.18653/v1/2021.nuse-1.8",
    pages = "72--83",
    abstract = "Automated storytelling has long captured the attention of researchers for the ubiquity of narratives in everyday life. The best human-crafted stories exhibit coherent plot, strong characters, and adherence to genres, attributes that current states-of-the-art still struggle to produce, even using transformer architectures. In this paper, we analyze works in story generation that utilize machine learning approaches to (1) address story generation controllability, (2) incorporate commonsense knowledge, (3) infer reasonable character actions, and (4) generate creative language."
}