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    Area of Science:

    • Digital Humanities
    • Computational Linguistics
    • Literary Studies

    Background:

    • Analyzing literary texts involves complex relationship mapping (characters, themes).
    • Extracting structured data from unstructured narratives is a significant challenge.
    • Advancements in Large Language Models (LLMs) offer new possibilities for text analysis.

    Purpose of the Study:

    • To leverage LLMs for automated narrative information extraction.
    • To develop an interactive visualization system for literary analysis.
    • To explore LLM capabilities in augmenting storyline visualization techniques.

    Main Methods:

    • Developed an LLM-driven data parsing pipeline for novels and scripts.
    • Created STORY RIBBONS, an interactive visualization system.
    • Conducted evaluations and user studies on 36 literary works.

    Main Results:

    • Demonstrated LLMs' potential to streamline narrative visualization creation.
    • Showcased STORY RIBBONS' ability to reveal insights into character and theme trajectories.
    • Identified current limitations of AI-based systems in literary analysis.

    Conclusions:

    • LLMs can significantly enhance the process of literary visualization.
    • Interactive systems like STORY RIBBONS facilitate deeper exploration of narrative structures.
    • Further research is needed to address AI limitations and refine interaction motifs.