@inproceedings{roemmele-2019-identifying,
    title = "Identifying Sensible Lexical Relations in Generated Stories",
    author = "Roemmele, Melissa",
    editor = "Bamman, David  and
      Chaturvedi, Snigdha  and
      Clark, Elizabeth  and
      Fiterau, Madalina  and
      Iyyer, Mohit",
    booktitle = "Proceedings of the First Workshop on Narrative Understanding",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/W19-2406/",
    doi = "10.18653/v1/W19-2406",
    pages = "44--52",
    abstract = "As with many text generation tasks, the focus of recent progress on story generation has been in producing texts that are perceived to {\textquotedblleft}make sense{\textquotedblright} as a whole. There are few automated metrics that address this dimension of story quality even on a shallow lexical level. To initiate investigation into such metrics, we apply a simple approach to identifying word relations that contribute to the {\textquoteleft}narrative sense' of a story. We use this approach to comparatively analyze the output of a few notable story generation systems in terms of these relations. We characterize differences in the distributions of relations according to their strength within each story."
}