Existing LLM-based systems for writing long-form stories or story outlines frequently suffer from unnatural pacing, whether glossing over important events or over-elaborating on insignificant details, resulting in a jarring experience for the reader. We propose a CONCrete Outline ConTrol (CONCOCT) system to improve pacing when automatically generating story outlines. We first train a concreteness evaluator to judge which of two events is more concrete (low-level-detailed). This evaluator can then be used to control pacing in hierarchical outline generation; in this work, we explore a vaguest-first expansion procedure that aims for uniform pacing. We further use the evaluator to filter new outline items based on predicted concreteness. Compared to a baseline hierarchical outline generator, humans judge CONCOCT's pacing to be more consistent over 57% of the time across multiple outline lengths; the gains also translate to downstream stories. All code, data, and models are open-sourced.

通过训练一个具有判定具体（低层次详细）程度的评估器，我们提出了一个名为CONCrete Outline ConTrol（CONCOCT）的系统，该系统在自动生成故事大纲时改善了情节掌控，通过模糊化的首次扩展过程来实现统一的情节掌控；评估器还可用于基于预测的具体程度对新的大纲项目进行过滤，相比基线分层大纲生成器，人们在多个大纲长度上57%的时间内判断CONCOCT的情节掌控更为一致，并且这种优势也体现在后续的故事中。

提升长篇故事规划中的节奏