Multi-Agent System for AI-Assisted Extraction of Narrative Arcs in TV Series

• URL: http://arxiv.org/abs/2503.04817v1
• Date: Tue, 04 Mar 2025 20:27:14 GMT
• Title: Multi-Agent System for AI-Assisted Extraction of Narrative Arcs in TV Series
• Authors: Roberto Balestri, Guglielmo Pescatore,
• Abstract summary: Serialized TV shows are built on complex storylines that can be hard to track and evolve in ways that defy straightforward analysis.<n>This paper introduces a multi-agent system designed to extract and analyze these narrative arcs.<n>Tested on the first season of Grey's Anatomy (ABC 2005-), the system identifies three types of arcs: Anthology (self-contained), Soap (relationship-focused), and Genre-Specific.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Serialized TV shows are built on complex storylines that can be hard to track and evolve in ways that defy straightforward analysis. This paper introduces a multi-agent system designed to extract and analyze these narrative arcs. Tested on the first season of Grey's Anatomy (ABC 2005-), the system identifies three types of arcs: Anthology (self-contained), Soap (relationship-focused), and Genre-Specific (strictly related to the series' genre). Episodic progressions of these arcs are stored in both relational and semantic (vectorial) databases, enabling structured analysis and comparison. To bridge the gap between automation and critical interpretation, the system is paired with a graphical interface that allows for human refinement using tools to enhance and visualize the data. The system performed strongly in identifying Anthology Arcs and character entities, but its reliance on textual paratexts (such as episode summaries) revealed limitations in recognizing overlapping arcs and subtler dynamics. This approach highlights the potential of combining computational and human expertise in narrative analysis. Beyond television, it offers promise for serialized written formats, where the narrative resides entirely in the text. Future work will explore the integration of multimodal inputs, such as dialogue and visuals, and expand testing across a wider range of genres to refine the system further.

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