  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.
