Year: 2023

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.

heading: 1 Introduction
text: What is the shape of a story? Narratologists have long been fascinated with reducing narratives to a
compelling linear visual rhetoric: the narrative arc,
a line chart that smoothly demonstrates the (emotional) rise and fall of the story (Freytag,
1895; Campbell, 1949; Propp, 1968). Recent
scholarship has introduced emotive expressions
and affect as a vital analytical tool for the construction of such narrative arcs (Kleres, 2011;
Keen, 2011; Winkler et al., 2023). The digital
humanities community has shown great interest in
operationalizing this problem as a sentiment
analysis task across various literary corpora (Jockers 2015; Underwood, 2015; Elkins, 2022).
The success of this approach has recently been
extended beyond the literary domain to encompass a wider range of inquiries driven by social science
(Boyd et al., 2020; Chun 2021).
Meanwhile, existing methods for sentimentbased narrative arc extraction tend to underperform
on what literary scholars call non-linear narratives
(Richardson, 2000). We posit that literary works often assume varying degrees of clarity and
straightforwardness when conveying a story, an
explicative quality known as narrativity —
computationally, it has been defined as a scalar measuring the success of a work in conveying a
linear sequence of events as narrative discourse
(Piper et al., 2021). While some novels may convey their story-worlds with relative transparency via
chronological accounts of their fictional agents’
actions, others may withhold it from the audience for artistic purposes (Pianzola, 2018). This non-
linearity has been considered a hard problem for
narratology, by both computational (Elkins and Chun, 2019; Bhyravajjula et. al, 2022) and
traditional (Ryan, 2005) approaches. Virginia
Woolf’s 1925 novel Mrs. Dalloway, in particular,
has been identified as an especially recalcitrant text
to model with existing methods (Elkins, 2022),
possibly due to its renowned stream-ofconsciousness style.
This study takes on the challenging task of
performing unsupervised plot extraction on Mrs. Dalloway, a novel widely held and celebrated by
literary scholars to be essentially “plotless.” We
hypothesize that it is possible to excavate latent plot structures from nonlinear fiction if we use
sentiment data to statistically model the notion of
non-linearity itself. To avoid the pitfall of imposing
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linear narrative arcs onto non-linear narratives via
smoothing-based de-noising techniques, we propose a sentiment-centric pipeline which instead
aims to embrace the “noise” inherent to a non-
linear and highly fragmented novel like Mrs. Dalloway. The goal of this pipeline is to capture the
full expression of non-linearity in sentiment data.
Leveraging the softmax probability distributions of pre-trained language models like BERT (Devlin et
al., 2019) and ELECTRA (Clark et al., 2020), we
perform a paired t-test that models sentiment’s rateof-change to identify breakpoints and
correspondingly segment the novel into
emotionally coherent parts. Our approach finds 19 such surrogate “chapters” in Mrs. Dalloway, which
we then qualitatively evaluate to assess their
literary and narratological coherence. To further verify the validity of our results, we quantitatively
evaluate our pipeline on a linear fiction dataset to
determine its ability to restore existing chapter structures, while also contributing a generative
approach to the task of text segmentation in the
literary domain.
Main contributions: 1) a segment-based approach
to plot extraction, designed to address the challenge
of modeling non-linear fiction 2) a sentimentcentric pipeline for fully unsupervised chapter
segmentation 3) an attempt to consider literary
theoretical claims as falsifiable hypotheses that could inform model design, in the hope for the
greater inclusion of literary scholarship in the
collaboration pipeline for NLP research.

heading: 2 Background and Related Work

heading: 2.1 Definitions
text: Our study will operate under the following
narratological definitions:
• Plot: We define plot not as a fixed
structure but a gradual process of
structuration, a dynamic development that
actualizes and amends itself as the narrative unfolds and constantly reshapes
the experience of reading (Brooks, 1984;
Phelan, 1989). The concept of structuration highlights the need to
examine not just the general distribution of
sentiment scores, but also as the relative rate of change between measured points of
sentiment.
• Linearity: Colloquially, linear narratives
often refer to storylines that are aligned
with chronological order. In narratology,
linearity is the side of plot that relates to causality (Forster, 1927), and linear
narratives are the framing of fictional
“action[s] as a chronological, cause-andeffect chain of events occurring within a
given duration and a spatial field”
(Bordwell, 1985:49). The metaphor of the “chain” necessitates something that comes
before together with a subsequent, a
sequence of events that becomes coherent
for the audience through the clear
cognition of time and a correspondence
between the two. Linearity, therefore, is the plot made visible via having its causal
sequences ordered in the plain sight of
chronological time.
• Sentiment: We borrow our definition of
sentiment from leading narrative theorist Patrick Hogan, who views emotion as the
hallmark of non-linearity: “our emotion
systems respond to perceptual fragments […] these cluster into incidents that
provoke emotional spikes in emotional
experiences that are, like time, not smoothly continuous but jagged,”
(2011:66). In making this claim, Hogan
draws a distinction between objective, universal clock time and our non-uniform
experience of temporality. Just as
objective time orders causal chains into a linear plot that makes sense to the
audience, subjective narrative time is
organized by emotional fluctuations into
coherent units, which we hope to segment with sentiment analysis. In the context of
our study, Hogan’s argument implies that
our sentiment analysis pipeline would be expected to extract a set of “jagged”
distributions from non-linear novels,
instead of a smooth line, to represent the non-linear narrative arc.
Hypothesis: Operationalizing Hogan’s (2011) theory of affective narratology, which heavily
emphasizes an underlying connection between
plot, non-linearity, and sentiment, we propose a conception of plot as a continuous process of
structuration with two components: the easily
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observable1, time-dependent arm as a causal chain
of events ordered in objective time, and the latent, time-independent arm as the fragmented, non-
linear, yet internally coherent, narrative arc
concealed in emotion. These two arms are not always present in all narratives. Rather, they are
two ways for a plot to be expressed, and if they
happen to coexist like in linear narratives, their structure tends to synchronize because they
essentially describe different aspects of the same
plot. This narratological unity they share enables the use of the observable arm as gold-standard
ground truth to validate inferences made from the
latent arm. Through a combination of qualitative and quantitative evaluations of our pipeline’s
output in Section 4, we aim to holistically validate
our use of sentiment as a plausible approach to plot extraction.

heading: 2.2 Narrative Arc Construction with Sentiment Analysis
text: Prior research in this area has heavily relied on
smoothing techniques to identify linear and human-readable patterns in the noisy sentiment
data of long-form texts. Jockers’ (2015) Syuzhet
utilizes fast Fourier transform and discrete cosine
transformation to extract sentiment arcs from its
lexicon-based sentiment models. Gao et al. (2016)
build on Jockers’ work by employing a more complex model for smoothing with an adaptive
filter. More recently, Chun (2021) proposes an
ensemble approach that combines the outcomes of multiple sentiment models to mitigate model and
dataset bias, while still requiring smoothing with
simple moving average. To the best our knowledge, we are the first study to extract narrative arcs from
sentiment data without any involvement of
smoothing. For our intent and purposes, smoothing
is problematic because it seeks to reduce non-linear
narratives to a clean yet oversimplified line.

heading: 2.3 Text Segmentation in Fiction
text: Since our pipeline outputs a segmented narrative
arc, it also contributes to the broader problem of
text segmentation in the literary domain. Recent studies have fine-tuned pre-trained language
models to perform chapter segmentation, and their
methods tend to use classification-based, reducing the problem to the binary classification of each
1 “Observable” here means both being visible on the page, i.e., chapter boundaries, and being causally
potential breakpoint candidate as a predicted
chapter boundary or not. Pethe et al. (2020) finetune BERT’s next sentence prediction model as a
binary classifier for chapter break prediction, and
use the inference’s confidence score to rank all breakpoint candidates in each novel to select the
top P as predicted chapter breaks, P being the
number of ground truth chapters. Their approach outperforms all non-transformer baselines.
Virameteekul (2022) further improves Pethe et al.’s
performance by utilizing a XLNet and a CNN model instead of BERT.
Although our quantitative evaluations in Section
4.2 perform the same task as these existing approaches, we cannot use them as baselines due to
significant differences in methods and
experimental setting. This includes: 1) our pipeline is fully unsupervised, without any knowledge of P
during inference 2) our sentiment models are not
fine-tuned any chapter break training data, and 3) the paired t-test in our study could only infer
segments on the multiples of the initial sequence
length alpha, making it arithmetically impossible
to locate most exact chapter breaks. Nonetheless, our study could be considered as a generative
approach to text segmentation, an alternative to
existing classification-based methods.

heading: 3 Methods
text: This section describes the experimental designs of our pipeline. It takes a text file of Mrs. Dalloway and outputs an unsmoothed narrative arc segmented into surrogate “chapters,” as shown in Figure 1 below.

heading: 3.1 Literary Domain Fine-Tuning for Sentiment Analysis
text: For sentiment analysis, we fine-tune a pretrained ELECTRA model on a Victorian fiction
sentiment dataset (Kim, 2022), the only open-
source fiction dataset we find with sentence-level
sentiment labels. ELECTRA is selected over BERT because it reports better performance on
benchmark sentiment analysis datasets (Clark et al,
2020). We also implement a popular BERT-based
sentiment model obtained from HuggingFace finetuned on product reviews (nlptown, 2019) as a
general-domain reference. Using an additional
model allows us to troubleshoot the question of
cognizable, i.e., the chain-of-thought summaries of “what happened” ordered chronologically.
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cross-domain consistency, as the performance of
many NLP systems has been demonstrated to “drop
precipitously” when applied to the literary domain (Bamman et al., 2019:2141). This is supported by
our model evaluations, as the ELECTRA model’s
testing metrics on its hold-out test set (accuracy=0.49, ovr AUC=0.734) significantly
outperforms that of the BERT model’s inference on
the same test set (accuracy=0.21, ovr AUC=0.613).
Inference: We run each sentence of Mrs. Dalloway (n=3626, no preprocessing) through the sentiment classifiers and generate a 5-dimensional probability distribution to represent the sentence’s sentiment (details in Section 4.2). Despite the gap in testing performance, we pass the output of both models into our subsequent pipeline to further experiment with cross-domain consistency, since recent studies have demonstrated that there is no one-size-fit-all sentiment model for constructing narrative arcs (Elkins, 2022).

heading: 3.2 Breakpoint Analysis with Statistical Testing
text: When implemented on non-linear narratives like
Mrs. Dalloway, past studies’ use of smoothing
(Section 2.2) often produces oversimplified and unconvincing results that lack explainability
(Figure 2a & 2b). To address this shortcoming, we
perform segment detection to extract a fully explainable narrative arc: any statistically
significant movement on the sentiment arc could be
traced back to the corresponding sentence where a breakpoint is identified, making it possible to
explain model decision with close reading, the gold
standard for evidentiary claims in literary studies
(Felski, 2008).
Dimensionality Transformation: The default output of both the BERT and the ELECTRA model is a sentiment score on a 5-point Likert scale, a monolithic representation that often fails to capture the nuanced sentiment of literary texts. The neutral sentiment label, in particular, is
(a) (b)
Figure 2. Mrs. Dalloway’s sentiment visualized with existing methods: (a) fast Fourier transform from Jockers’ Syuzhet, (b) Loess smoothing (span=0.3)
Figure 1. Segmented narrative arc the BERT model extracts from Mrs. Dalloway, with 19 predicted chapters (use of the same color does not indicate the continuation of the same chapter)
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responsible for many trivial zero values in narrative arc plotting whose presence does not necessarily correlate with actual emotional neutrality (Elkins, 2022). To avoid this pitfall and mitigate the issue of data oversimplification, we configure the sentiment models to directly output the 5-dimensional probability vector, instead of taking the argmax of the softmax probabilities of each sentence. This approach allows for a more holistic representation of ambiguous sentiment by transforming a discrete sentiment scale into a continuous one, as shown in Table 1. We also experiment with the unnormalized logits tensors prior to the softmax operation and observe similar outcomes in subsequent procedures.
To preprocess and denoise the 5-dimensional
sentiment scores for segment detection, we utilize
principal component analysis (PCA) to identify the two most significant emotional dimensions of any
given distribution that are linearly independent
from each other. PCA is an orthogonal linear transformation technique that renders the greatest
variance of all projections of the data onto the first
coordinate, then the second greatest variance on the second coordinate, and so on. This simplifies the
dataset while maximizing data preservation, as
PCA finds that the first two principal components could explain more than 99% of the variability in
both the BERT and the ELECTRA model’s sentiment predictions on Mrs. Dalloway2. We only keep these two dimensions, given that they contain
the most substantial information regarding the data
and contribute the highest variance. Since each pair of PC1 and PC2 can be traced back to their
originating sentence, PCA allows for a more
explainable interpretative framework than previous
2 This is also the case for all other novels we use for quantitative evaluations in Section 4.
studies’ use of smoothing and numerical filtering
techniques.
Segment Detection: We design a statistical model that recurrently performs paired samples ttests to trace sentiment’s rate-of-change across a novel and predict potential locations for breakpoints. The t-test draws from sentence-level sentiment scores and groups them by paragraph. For one group of sentiment scores from passage 𝑃1 with an average of 𝑀1 and its next group 𝑃2 with an average of 𝑀2, the null hypothesis 𝑀1 = 𝑀2 is assumed to be true unless the p-value is less than a critical value of 0.1, in which case the alternative hypothesis 𝑀1 ≠ 𝑀2 would be established. Since a paired test would require the two groups P1 and P2 to have the same length, the model would also take one hyperparameter alpha, the number of paragraphs in each passage. To give the model a higher degree of freedom, we set alpha=5 for inference on Mrs. Dalloway, since the end of the fifth paragraph of the novel is qualitatively determined to be the earliest semantic focal point to break off the initial “chapter.” From here on, the model treats each following alpha paragraphs as the 𝑃2 and evaluates it against P1, concatenating them into a longer 𝑃1 if the difference is not statistically significant, and marking a new chapter if it is while simultaneously making the paragraph in question the new 𝑃1. Aside from our definition of plot as structuration, our decision to focus on the rate-of-
change is also motivated by narratologist Tzvetan
Todorov’s equilibrium-disruption model (Todorov, 1971). Todorov posits that literary narratives
typically 1) start out in a state of stability, 2)
disrupted by an often unexpected event, and 3)
iterate through multiple attempts to restore the initial equilibrium as new disruptions arise. This
interplay between equilibrium and disequilibrium
is often accompanied by rapid shifts in emotions, or fluctuations in sentiment scores that our pipeline
captures as a non-linear analog of these
movements. Our approach lends especially well to Mrs. Dalloway, as literary scholars have noted that
such “interrupt[ions]” and “reinstate[ments]” occur
recurrently in Woolf’s fiction (Richardson, 2000: 686). These interruptions occur in a cyclic manner
emblematic of the novel’s non-linearity.
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heading: 4 Results and Discussion
text: To holistically evaluate the validity and limitations
of our pipeline’s output on Mrs. Dalloway shown in Figure 1, we follow the approach of Wang and
Iyyer (2019) to present the outcomes for literary
close reading alongside quantitative metrics.

heading: 4.1 Qualitative Evaluations
text: We perform a domain expert review of the predicted chapter divisions. Contrary to our
expectations, the general domain BERT model
returns more explainable results on Mrs. Dalloway when being compared against the ELECTRA
model, extracting the boundaries of 19 reasonable
segments that could be thought of as surrogate “chapters” (Table 6). This suggests that domain-
specific fine-tuning with the Victorian fiction
dataset could not be transferred to Mrs. Dalloway,
a modernist and non-linear novel. By extension, different literary time periods could be considered
as different domains, which is supported by the
conclusion of an existing body of research in digital humanities (Underwood, 2013).
Our pipeline succeeds in capturing recurrent
disruptions in fictional narratives. We observe that six of the sentences marking the beginning or
ending of our predicted segments involve Dr.
Holmes or Sir William Bradshaw, both of whom are particularly disruptive characters heavily
involved in the suicide of Septimus, one of the
novel’s protagonists. Peter’s conversation with Sally in “chapters” 17 and 18, for instance,
represents how a thematically coherent whole
could be interrupted by the appearance of the Bradshaws at Clarissa’s party. Similarly, Bradshaw
being sent for at the beginning of “chapter” 12
interrupts Septimus’ last happy moment with Rezia paragraphs before his suicide. Moreover, we note
that the first appearance of Holmes that ends
“chapter” 7 opens the scene that formally initiates Septimus’ radical downward spiral. While Woolf
scholars have hypothesized the Bradshaws as the
vital link between the Clarissa and Septimus storylines (Joyes, 2008), our findings take this a
step further by dissecting the novel through its
affective substratum to show his structural
significance on an empirical level. We provide the detailed predicted chapters list in the appendix.
3 Some non-linear novels are segmented into sections or parts that are not labeled as chapters by the author. They are usually done out of editorial convenience,

heading: 4.2 Quantitative Evaluations
text: Data: For quantitative evaluation, we assemble two fictional datasets from Project Gutenberg (Gutenberg, n.d.): 1) 9 linear novels to vertically validate our pipeline’s ability to accurately segment fictional narratives, 2) 4 additional nonlinear novels to horizontally validate our findings in Mrs. Dalloway. For the purposes of quantitative testing, we define linear fiction as novels already divided into chapters by their authors. Conversely, non-linear fiction refers to novels published without existing chapter structures 3 , usually out of aesthetic choices (Pianzola, 2018), accompanied by a greater degree of narrative fragmentation that makes them harder to model. Table 2 demonstrates the relation between these surface-level operational definitions and their conceptual counterparts defined in Section 2.1: by definition, non-linear novels do not have linear plot, while linear novels contain both, one observable (chapters) and one latent (sentiment). The non-linear narrative arc that our pipeline extracts is not mutually exclusive with linear narrative features like chapters — linear novels, too, are often embedded in latent emotional spaces, carrying a hidden sentiment arc that coexists alongside the linear organization of plot through chapters.
The linear fiction dataset only contains Victorian
novels, to maintain domain consistency with the ELECTRA model’s fine-tuning set. We use
Chapterize (Reeve, 2016) to extract from each
novel’s Gutenberg text file a list of paragraph indices that represent the locations of chapter
breakpoints. All 9 lists are then manually curated to
and do not have a chapter’s commitment to thematic coherence and fictional causality. Therefore, we do not consider them as ground truth segmentations.
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ensure that the annotations of chapter boundaries
are correct, a step necessary due to the known header alignment issues in Project Gutenberg
documents (Pethe et al., 2020). The curated output
will be considered as the gold-standard ground truth labels for chapter segmentation.
Chapter Segmentation: The predicted chapter segmentation results from 4.1 could not be directly evaluated with quantitative metrics, due to the absence of author-assigned ground truth chapter segmentation labels in Mrs. Dalloway. To overcome this limitation, we opt for indirectly evaluating our results, by testing the ability of our pipeline to restore the existing chapter boundaries of linear novels. In doing so, we hope to validate our approach of extracting emotive plot itself, that it is indeed a form of plot, and generally of its linear counterpart.
We remove all chapter headers and related signals from the texts (the only input text preprocessing step in our study) and apply our pipeline to the linear fiction dataset. To match the inference with the format of the dataset’s ground truth chapter segmentations for evaluation, we adjust the pipeline to output from each novel a list of paragraph indices where each predicted chapter begins.
We follow Pethe et al.’s use of F14 to report the
performance of exact break prediction, with one key caveat: due to the nature of the paired samples
t-test, the only potential breakpoint candidates
would be the multiples of the hyperparameter
alpha, which makes it arithmetically impossible for our pipeline to predict the exact location of most
4 Since our pipeline is not supervised with the correct number of chapters, it may not predict the same number of segments as the ground truth. This constraint does not meet the input data requirements
pipeline, we also compute a general area F1, where the locations of ground truth chapters are rounded
up to the closest multiple of alpha.
Table 3 compares the performance of our
pipeline when utilizing the ELECTRA and BERT sentiment models, with alpha set to 5 to remain
consistent with the findings of Section 4.1. The
literary domain ELECTRA model significantly outperforms the general domain BERT model. To
further substantiate this result, we incorporate the
following baselines into our evaluation:
• Random: P breakpoints are randomly
selected from each novel, where P denotes the number of ground truth chapters.
• Dummy regressor: P breakpoints are
randomly selected from all available multiples of alpha in each novel. This baseline is designed as an ablation of the use of sentiment analysis.
Since both baselines are randomly generated, we
report their average F1 over 10 iterations. Even with the hint of P provided as supervision, the
baselines’ performance remains insignificant. This
validates the complexity of the task and the effectiveness of our pipeline.
Table 4 reports the performance of the
ELECTRA model on each novel when F1 is not
for other commonly used metrics in text segmentation. Evaluative approaches that we are unable to appropriately utilize include sliding window-based methods, inter-annotator agreement measures, and geometric distances.
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fixed. To explore alpha as a tunable hyperparameter, we experiment with alpha values ranging from 1 to 30, and use the exact location F1 to select the optimal value to compute the general area F1. The improvement provided by the optimal alpha is not significant, as the exact location F1 of most alpha values tend to be similar. A smaller alpha results in a larger number of predicted breaks, covering more ground truth breaks (true positives), while also predicting more breaks where one does not exist (false positives). Conversely, a larger alpha means fewer predicted breaks, fewer correct predictions, but also fewer mistakes. Nonetheless, the optimal alpha has a significant impact on the accuracy of predicting the number of chapters. The inference of 5 of the 9 optimal alpha falls within plus/minus 1 of the ground truth chapter count P, while all alpha values from 1 to 30 average a Manhattan difference of 53 from P.
Using the optimal alpha, the ELECTRA model achieves a general area F1-score of 0.643, indicating its ability to predict the location of most chapter boundaries within the margin of a few paragraphs, which is more than adequate given the room for ambiguity in literary works. Our quantitative findings validate the hypothesis put forward in Section 1 that the emotional patterns underlying fictional narratives often correspond with the linear arm of plot, evident in the number of breakpoints that sentiment analysis shares with the existing chapter segmentations of linear novels. This correspondence, in turn, supports the validity of using the sentiment-centric pipeline for inference on non-linear novels like Mrs. Dalloway, where the visualizations like Figure 1 serve as the surrogate of linear plot by making a novel’s latent emotional space observable.
4.3 Towards Quantifying Non-Linearity in Fiction
Table 5a compares the distribution of predicted chapter lengths (counted by the paragraph) in the linear and non-linear fiction datasets, with alpha set again to 5 to maintain consistency with previous experiments. Notably, the lengths of
counterparts. However, their coefficient of variation (CV), a metric measured against the mean, does not exhibit a significant difference. This suggests that non-linear novels have more variable chapter lengths compared to linear ones in terms of absolute variability, while the relative variability of the two groups is similar.
We validate this pattern with the baselines from Section 4.2. As Table 5b shows, the random and dummy baselines also produce similar CVs and different variances between linear and non-linear fiction, though the difference is less substantial than that of the sentiment models. This indicates that the difference in variance pertains to the two fictional corpora instead of methods for extraction. Furthermore, the fact that all 4 models produce similar CVs might undermine its effectiveness as a metric in this experiment.
One potential explanation for the discrepancy between variance and CV is that our pipeline identifies more outlier chapters in non-linear novels. The 1% longest chapters the ELECTRA model extracts from the non-linear set contain 7.8% of all paragraphs in the corpus, compared to 5.8% for the chapters in the 99th percentile in length obtained from the linear set. The length of “chapters” in non-linear novels is not constrained by the need to fit a linear plot, therefore containing more outliers that lead to greater variability and fragmentation.
This result further validates our findings in Section 4.1. The BERT model that outputs Figure 1 reports Var=1647.84 and µ=28.15 from the lengths its predicted chapters on Mrs. Dalloway, which are consistent with the averages of the nonlinear fiction dataset (Var=1509.62, µ=34.97). This offers some support for the generalizability of the outcomes of Section 4.1 to other non-linear novels, if similar qualitative analysis is to be performed on them by domain experts.

heading: 5 Conclusion and Future Work
text: With a pipeline capable of excavating nonlinear plot from both non-linear and linear novels, this study takes the first steps to 1) investigate the
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hypothesis proposed in Section 1, and 2) explore the positive impact literary theory could have on model design for narrative understanding. We demonstrate that it is possible to extract a narrative arc with coherent segments from nonlinear narratives like Mrs. Dalloway, and the explainability of our approach affords actionable outcomes for literary studies—explainable results promote empirical theory testing. We validate our findings with both qualitative and quantitative evaluations, achieving a F1 0.643 (general area) and a 0.214 (exact) after hyperparameter tuning. In doing so, we also uncover some evidence for a potential correspondence between the linear (chronological, causal) and nonlinear (emotional) arms of plot in the linear fiction dataset. We further discover that the chapters we extract from non-linear fiction tend to vary more in length, which we understand as a corpus-level difference.
The qualitative analysis in Section 4.1 shows that the general domain BERT model produces more explainable results than the ELECTRA model fine-tuned on Victorian novels, while ELECTRA quantitatively outperforms BERT in Section 4.2. This is not so much a contradiction as a guidance for future research: perhaps the “literary domain” is not a monolith, but an umbrella term for a collection of domains that are significantly different from each other. Is the domain barrier between linear and non-linear fiction? If so, then ELECTRA could be considered as an “in-domain” model for experiments in 4.2 because the object of inference is linear fiction, while not for 4.1 since it concerns non-linearity. It is possible that if ELECTRA is fine-tuned on a non-linear fiction dataset with sentiment labels, it could further improve upon the findings of 4.1.
Aside from these questions, other potential directions for our future work include 1) designing more robust methods for quantifying non-linearity in fiction, which could be leveraged for a wide range of inquiries in digital humanities, 2) combining our sentiment-based pipeline with existing semantic-based approach to improve the performance of chapter segmentation, and 3) expanding this research to narratives beyond the literary domain, which is also a key interest of contemporary narratology. We also hope to open up the discussion of non-linearity in fictional narratives to event-centric and character-centric approaches to better understand the interplay between causal and emotional dimensions of plot.

heading: 6 Limitations
text: Due to various constraints, our experiments are only able to cover five non-linear novels and nine linear novels, listed in Table 7. This pales in comparison to the thousands of novels typically expected of large-scale studies in digital humanities, whose scale allows them to make generalizable claims regarding narratives or literary history (Piper et al., 2021). We hope to make up for this gap in our future work. One key challenge to scaling our dataset would be data availability. The use of non-linearity in fiction is predominantly a 20th century phenomenon, which suggests that many non-linear novels will not be in the public domain for some time to come.
In terms of experiment design, an important limitation of the quantitative evaluations in Section 4.2 is its assumption that a novel’s chapter divides provided by its author could be thought of as a form of “gold standard” labels for model validation. This claim of authorial control and “authority” over the text has been thoroughly problematized in literary studies since the emergence of poststructuralism (Barthes, 1967; Foucault, 1969), while analogous suspicions have been raised in natural language generation against the assumed reliability of human evaluators (Clark et al., 2021). Unfortunately, the author’s input is the only operationalizable criteria for ground truth available to us within the scope of this study.

heading: Acknowledgement
text: We sincerely thank Fangyuan Xu, the workshop organizers, and the anonymous reviewers for their generous time, attention, and helpful feedback on this paper. Peiqi Sui, Lin Wang, Kelvin Wong, and Stephen T. Wong were supported by T. T. & W. F. Chao Foundation and the John S Dunn Research Foundation.

heading: A Additional Tables
text: Chapter Scene/Event Starting Sentence(s) Ending Sentence(s) Chapter
length (paragraphs)
11 Septimus fears the arrival of
Holmes and Bradshaw
“He started up in terror.”
“But this hat now. And then (it was getting late) Sir William Bradshaw.”
9
12 Rezia shares a beautiful
moment with Septimus before leaving; Holmes arrives the apartment; Septimus commits suicide
“She held her hands to her head, waiting for him to say…”
“‘I'll give it you!’ he cried, and flung himself vigorously, violently down on to Mrs. Filmer's area railings.”
13
13 Guests arriving at the party “‘The coward!’ cried
Dr. Holmes, bursting the door open.”
“She could not resist recalling what Charles Darwin had said about her little book on the orchids of Burma.”
105
14 Clarissa talking to Lady
Bruton about her lunch with Richard
“(Clarissa must speak to Lady Bruton.)” “(Lady Bruton detested illness in the wives of
politicians.)”
5
15 Peter’s arrival at the party;
Clarissa wants to talk but could not
“‘And there's Peter Walsh!’ said Lady Bruton”
“... she must go up to Lady Bradshaw and say . . .”
8
16 Clarissa hosting the party, then
learns about Septimus’ death and withdraws
“But Lady Bradshaw anticipated her.” “She must assemble. She must find Sally and
Peter. And she came in from the little room.”
29
17 Peter’s conversation with
Sally
“But where is Clarissa?’ said Peter. He was sitting on the sofa with Sally.”
“He made Sally laugh.” 40
18 Peter and Sally looking at
Elizabeth
“But Sir William Bradshaw stopped at the door to look at a picture.” “What is it that fills me with extraordinary excitement?”
5
19 “It is Clarissa, he said. For
there she was.”
“It is Clarissa, he said.”
“For there she was.” 2
Table 6 (continue). Full list of predicted chapters (BERT) in Mrs. Dalloway (continues from last page). The corresponding narrative arc is displayed in Figure 1.
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heading: Future Work
text: We hope to make up for this gap in our future work. One key challenge to scaling our dataset would be data availability. The use of non-linearity in fiction is predominantly a 20th century phenomenon, which suggests that many non-linear novels will not be in the public domain for some time to come. In terms of experiment design, an important limitation of the quantitative evaluations in Section 4. 2 is its assumption that a novel’s chapter divides provided by its author could be thought of as a form of “gold standard” labels for model validation. This claim of authorial control and “authority” over the text has been thoroughly problematized in literary studies since the emergence of poststructuralism (Barthes, 1967; Foucault, 1969), while analogous suspicions have been raised in natural language generation against the assumed reliability of human evaluators (Clark et al. , 2021). Unfortunately, the author’s input is the only operationalizable criteria for ground truth available to us within the scope of this study