CHIRON: Rich Character Representations in Long-Form Narratives
This addresses the issue of poor character understanding in story analysis and generation systems, offering a domain-specific solution for narrative processing.
The paper tackles the problem of representing complex characters in long-form narratives by proposing CHIRON, a character sheet-based representation that improves masked-character prediction, showing better performance and flexibility than summary-based baselines.
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.