@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.",
}
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%0 Conference Proceedings
%T Mrs. Dalloway Said She Would Segment the Chapters Herself
%A Sui, Peiqi
%A Wang, Lin
%A Hamilton, Sil
%A Ries, Thorsten
%A Wong, Kelvin
%A Wong, Stephen
%Y Akoury, Nader
%Y Clark, Elizabeth
%Y Iyyer, Mohit
%Y Chaturvedi, Snigdha
%Y Brahman, Faeze
%Y Chandu, Khyathi
%S Proceedings of the 5th Workshop on Narrative Understanding
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F sui-etal-2023-mrs
%X 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.
%R 10.18653/v1/2023.wnu-1.15
%U https://aclanthology.org/2023.wnu-1.15
%U https://doi.org/10.18653/v1/2023.wnu-1.15
%P 92-105
Markdown (Informal)
[Mrs. Dalloway Said She Would Segment the Chapters Herself](https://aclanthology.org/2023.wnu-1.15) (Sui et al., WNU 2023)
ACL