CHIRON: Rich Character Representations in Long-Form Narratives
<|reference_start|>CHIRON: Rich Character Representations in Long-Form Narratives: Characters are integral to long-form narratives, but are poorly understood by existing story analysis and generation systems. While prior work has simplified characters via graph-based methods and brief character descriptions, we aim to better tackle the problem of representing complex characters by taking inspiration from advice given to professional writers. We propose CHIRON, a new `character sheet' based representation that organizes and filters textual information about characters. We construct CHIRON sheets in two steps: a Generation Module that prompts an LLM for character information via question-answering and a Validation Module that uses automated reasoning and a domain-specific entailment model to eliminate false facts about a character. We validate CHIRON via the downstream task of masked-character prediction, where our experiments show CHIRON is better and more flexible than comparable summary-based baselines. We also show that metrics derived from CHIRON can be used to automatically infer character-centricity in stories, and that these metrics align with human judgments.<|reference_end|>
@article{gurung2024chiron:,
    title={CHIRON: Rich Character Representations in Long-Form Narratives},
    author={Alexander Gurung and Mirella Lapata},
    journal={arXiv preprint arXiv:2406.10190},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10190},
    primaryClass={cs.CL}
}