@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 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 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
- Peiqi Sui, Lin Wang, Sil Hamilton, Thorsten Ries, Kelvin Wong, and Stephen Wong. 2023. Mrs. Dalloway Said She Would Segment the Chapters Herself. In Proceedings of the The 5th Workshop on Narrative Understanding, pages 92–105, Toronto, Canada. Association for Computational Linguistics.