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.

翻译：角色是长篇叙事不可或缺的组成部分，但现有故事分析与生成系统对其理解尚浅。先前研究多通过基于图的方法和简短角色描述来简化角色，而我们则从专业作家的创作建议中汲取灵感，旨在更好地解决复杂角色的表征问题。我们提出了CHIRON——一种基于“角色档案”的新型表征方法，用于组织和筛选关于角色的文本信息。CHIRON档案的构建分为两步：首先由生成模块通过问答形式提示大语言模型获取角色信息，随后验证模块利用自动推理和领域特定蕴含模型来剔除关于角色的错误事实。我们通过掩码角色预测这一下游任务验证CHIRON的有效性，实验表明CHIRON相比基于摘要的基线方法具有更优性能和更高灵活性。我们还证明，从CHIRON衍生的度量指标可用于自动推断故事中的角色中心性，且这些指标与人类判断具有一致性。