Can AI agents analyze TV show narratives?
Multi-Agent System for AI-Assisted Extraction of Narrative Arcs in TV Series
This paper presents a multi-agent system designed to automatically extract and categorize narrative arcs (ongoing storylines) from serialized media like TV shows, using Grey's Anatomy as a test case. It leverages LLMs (specifically GPT-4) for tasks like text simplification, character identification, and semantic analysis of plot summaries. The system uses multiple specialized agents working sequentially, each focusing on a different aspect of narrative analysis (e.g., identifying self-contained storylines, ongoing character relationships, or genre-specific plots). Results are stored in both relational and vector databases, with a graphical user interface allowing human refinement and correction of the LLM-generated outputs. Key findings include high accuracy in identifying self-contained episodes and characters, but difficulty with more complex, overlapping storylines, highlighting the need for combined human-computer analysis. The reliance on plot summaries (paratexts) limits the system's ability to capture subtle narrative details present in dialogue or visuals, but also makes it highly adaptable to text-based serialized narratives like novels or web fiction.