MARCUS: An Event-Centric NLP Pipeline that generates Character Arcs from Narratives
This provides a quantitative representation of character arcs for literary studies, enabling applications like trope identification and narrative comparison, though it appears incremental as it builds on existing NLP techniques for a new domain-specific task.
The paper tackles the novel task of computationally generating event-centric, relation-based character arcs from narratives, presenting MARCUS, an NLP pipeline that extracts events, characters, emotion, and sentiment to model inter-character relations and generate graphical plots, evaluated on Harry Potter and Lord of the Rings series.
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.