@inproceedings{sui-etal-2023-mrs,
    title = "Mrs. Dalloway Said She Would Segment the Chapters Herself",
    author = "Sui, Peiqi  and
      Wang, Lin  and
      Hamilton, Sil  and
      Ries, Thorsten  and
      Wong, Kelvin  and
      Wong, Stephen",
    editor = "Akoury, Nader  and
      Clark, Elizabeth  and
      Iyyer, Mohit  and
      Chaturvedi, Snigdha  and
      Brahman, Faeze  and
      Chandu, Khyathi",
    booktitle = "Proceedings of the 5th Workshop on Narrative Understanding",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.wnu-1.15",
    doi = "10.18653/v1/2023.wnu-1.15",
    pages = "92--105",
    abstract = "This paper proposes a sentiment-centric pipeline to perform unsupervised plot extraction on non-linear novels like Virginia Woolf{'}s Mrs. Dalloway, a novel widely considered to be {``}plotless. Combining transformer-based sentiment analysis models with statistical testing, we model sentiment{'}s rate-of-change and correspondingly segment the novel into emotionally self-contained units qualitatively evaluated to be meaningful surrogate pseudo-chapters. We validate our findings by evaluating our pipeline as a fully unsupervised text segmentation model, achieving a F-1 score of 0.643 (regional) and 0.214 (exact) in chapter break prediction on a validation set of linear novels with existing chapter structures. In addition, we observe notable differences between the distributions of predicted chapter lengths in linear and non-linear fictional narratives, with the latter exhibiting significantly greater variability. Our results hold significance for narrative researchers appraising methods for extracting plots from non-linear novels.",
}

This paper proposes a sentiment-centric pipeline to perform unsupervised plot extraction on non-linear novels like Virginia Woolf{'}s Mrs. Dalloway, a novel widely considered to be {``}plotless. Combining transformer-based sentiment analysis models with statistical testing, we model sentiment{'}s rate-of-change and correspondingly segment the novel into emotionally self-contained units qualitatively evaluated to be meaningful surrogate pseudo-chapters. We validate our findings by evaluating our pipeline as a fully unsupervised text segmentation model, achieving a F-1 score of 0.643 (regional) and 0.214 (exact) in chapter break prediction on a validation set of linear novels with existing chapter structures. In addition, we observe notable differences between the distributions of predicted chapter lengths in linear and non-linear fictional narratives, with the latter exhibiting significantly greater variability. Our results hold significance for narrative researchers appraising methods for extracting plots from non-linear novels.
Sui, Peiqi Wang, Lin Hamilton, Sil Ries, Thorsten Wong, Kelvin Wong, Stephen
Mrs. Dalloway Said She Would Segment the Chapters Herself