Character arcs are important theoretical devices employed in literary studies to understand character journeys, identify tropes across literary genres, and establish similarities between narratives. This work addresses the novel task of computationally generating event-centric, relation-based character arcs from narratives. Providing a quantitative representation for arcs brings tangibility to a theoretical concept and paves the way for subsequent applications. We present MARCUS (Modelling Arcs for Understanding Stories), an NLP pipeline that extracts events, participant characters, implied emotion, and sentiment to model inter-character relations. MARCUS tracks and aggregates these relations across the narrative to generate character arcs as graphical plots. We generate character arcs from two extended fantasy series, Harry Potter and Lord of the Rings. We evaluate our approach before outlining existing challenges, suggesting applications of our pipeline, and discussing future work.
Characters in narratives attempt to influence their circumstances to resolve conflict, while
circumstance itself shapes the characters with events that develop them [
The field of computational literary studies aims to understand, represent and generate
narratives. Existing research has focused on various perspectives, notably plot units [
Our approach difers from recent work in the closely related field of extracting relationships
between characters in terms of goals and linguistic features. Some existing approaches either
lack the nuance of emotions [
We borrow from [
We take inspiration from [
As the story progresses, the relationship between a pair of characters evolves. A relation arc is a plot of the shift in circumstance of a directed pair of characters across their participatory events in a narrative. While the actor of an event influences the circumstances that afect the experiencer, the latter’s subsequent actions are captured in other relation arcs, allowing the efect of events to trickle through multiple connected arcs forming the plot of a narrative. We posit that a character’s journey at any point in the narrative can be represented by contextually assessing the amalgamated efect of their interactions with both themselves and other characters on their circumstance; a character arc is thus defined as a pair of quantitative aggregations of all the corresponding relation arcs of a character as both actor and experiencer respectively.
Fig 1 shows the character arc for the protagonist of the Lord of the Rings, Frodo Baggins, capturing the shift of circumstances he experiences as both an actor and experiencer. The events Frodo participates in change his circumstances - a drop to the valleys (minimas) represents a deterioration of circumstances, whereas a rise to the peaks (maximas) denotes an improvement. It is evident from C and the arc slice between D and E that actor and experiencer arcs of a character are not always aligned, allowing for the intuitive explanation of instances when characters oppose the circumstances they find themselves in, for better or for worse.
We present MARCUS (Modelling Arcs for Understanding Stories), an event-centric NLP pipeline1 that generates character arcs from narratives. In this section, we will elaborate upon the various components of the pipeline.
We failed to retrieve meaningful character arcs in our preliminary experiments with short stories containing less than 500 events. Consequently, we choose two larger datasets for character arc analysis instead, namely The Harry Potter septology and The Lord of the Rings trilogy. For each source, we ensure that we remove page numbers, unnecessary annotations, unreadable characters, weblinks, etc. The details of each dataset are detailed in Table 1. 1https://github.com/darthbhyrava/MARCUS Novel
BookNLP
Event Extraction
Participant Extraction
Circumstance
Relation Arc
Character Arc Emotion Identification Sentiment Identification
Scoring
BookNLP [
We feed the features processed from BookNLP into a tagger consisting of a BiLSTM model with
BERT embeddings to extract events and corresponding entities from the narrative. The tagger
is trained3 using the LitEvents dataset [
We process each event with a BERT-based Semantic Role Labeller provided by AllenNLP4 to extract the latent predicate-argument structure of the event and identify roles of participant characters. If there are multiple events in the same sentence with the same actors and experiencers, we consider only the first event to avoid redundancy.
Every actor/experiencer pair corresponding to an event in the narrative is assigned a quantitative measure of circumstance. We argue that proxy indicators such as sentiment and implied emotion implicitly capture an event’s circumstances and are specifically well suited to our focus on shift of circumstance . Absolute measures of circumstance can therefore be interpreted as a characteristic of the genre or trope of narrative, while their shifts are a characteristic of the character’s journey. Since circumstances evolve over time (or event sequences), MARCUS considers the efect of previous circumstances between characters when defining relation arcs. 2https://github.com/dbamman/book-nlp 3https://github.com/dbamman/litbank 4https://docs.allennlp.org/models/main/models/structured_prediction/models/srl_bert/
We pose sentiment extraction as a regression task to capture the subtleties of relationships. We
ifne-tune a RoBERTa model on the Stanford Sentiment Treebank (SST) [
Sentiment alone may not give us enough information about circumstance - we argue that in
such cases, multi-faceted emotional states help capture shift in circumstance by leveraging
the nuances of relationships. To identify emotions, we use a BERT model5 trained on the
GoEmotions [
MARCUS generates relation arcs by plotting the measure of circumstance, , for every event, ,
belonging to an actor/experiencer pair of characters across the narrative.
(1)
e
(2)
e = ∗ e + ∑ ∗ ie
=1
where e is the measure of circumstance for event , ∈ (0, 1) is the sentiment co-eficient that
controls how much influence the fine grained sentiment score should have over relation arcs,
∈ (0, 1) is the sentiment of that event, is the total number of emotion labels for that event,
i ∈ [
We apply a window function (.) over te, the set of all measures of circumstance corresponding to the event set e, to calculate the relation arc, r, given by
r = ( te, , ) where (.) is the window function that helps smoothen the arcs while retaining previous state information, is the window size, and is an optional parameter for specifying order for polynomial fitting.
As shown in Fig 3a, the relation arcs are too noisy without smoothing or retention of previous circumstance information. We experiment with three standard window functions: Rolling 5https://github.com/monologg/GoEmotions-pytorch (a) (b)
Mean, Triangular Rolling Mean and Savitzky-Golay Filter [
We ask a set of 16 human volunteers (avid fiction readers aged 20-31) to peruse both the Harry Potter and Lord of the Rings series, following which they evaluate our system by answering surveys on two tasks: idchar, where the volunteers are given a list of relation arcs and character pairs and asked to match the arcs to their corresponding pairs, and idplot, where the volunteers are given character arcs, pertinent plot events and asked to identify the points in the arc that they think represent the corresponding events.
For both these tasks mentioned above, we calculate accuracy. Task idchar achieved an accuracy of 71.5% and task idplot had an accuracy of 72.9%. We also evaluate with the Fleiss Kappa metric where category 1 indicates a complete match, and 0 indicates otherwise. For both the tasks, we have a score of 0.675 and 0.528 indicating strong and moderate inter-annotator agreement, respectively. Thus, most of the volunteers consistently identified both the character pairs and the relevant points in the graph given the plot sequence.
We have a volunteer extremely familiar with the story annotate the first 300 events of Lord of the Rings trilogy involving Frodo Baggins as a participant character. The annotator marks each event with three labels denoting a positive, neutral or negative shift in circumstance. Tables 3 and 4 illustrate the positive and negative shifts as tagged by our system and the corresponding gold labels for the same events provided by our annotator. Our system tends to assign positive labels to neutral events and has higher accuracy for negative shifts of circumstance.
We identify five notable challenges in our approach that can be addressed in future work. Firstly, since MARCUS is a sequential pipeline, it is challenging to determine the efect of errors cascading through the system quantitatively. Secondly, our rolling window makes the arcs dependent on the availability of data; event paucity in short stories or characters with low interactions hinders accurate arc generation. Thirdly, we observe in our arcs that our ifne-grained events do not represent an abstract view of the discourse - a more contextual representation of events is needed. Fourth, in our understanding of a character’s circumstance, localized interactions with other event-specific characters heavily influence shifts; we need an efective means of capturing the latent relative importance of character-specific interactions. And lastly, our approach does not aim to handle non-linear narratives where events are not sequentially presented.
Providing tangibility to the theoretical concept of character arcs, MARCUS can be employed in a variety of applications. Character arcs can be used in a more nuanced approach for detecting similarity between narratives by focusing on character journeys, leading to a possible improvement in book recommendations and movie recommendations based on stories and scripts. Character arcs can also help with digital enrichment in e-readers, adding to the rich metadata provided by devices like Kindle. Character arcs can also function as guidance for natural language generation tasks in the field of fiction. And lastly, they can help narrative studies by identifying character tropes and for identification of personality traits.
We propose MARCUS (Modeling Arcs for Understanding Stories), an NLP pipeline that addresses the novel task of generating character arcs from narratives. We explain key concepts like events and circumstance and delve into the details of our event-centric approach which leverages proxy markers like sentiment and emotion. We then evaluate our pipeline, discuss challenges, elucidate future work and outline potential applications.