@inproceedings{wang-etal-2023-improving-pacing,
    title = "Improving Pacing in Long-Form Story Planning",
    author = "Wang, Yichen  and
      Yang, Kevin  and
      Liu, Xiaoming  and
      Klein, Dan",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.findings-emnlp.723/",
    doi = "10.18653/v1/2023.findings-emnlp.723",
    pages = "10788--10845",
    abstract = "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 **CONC**rete **O**utline **C**on**T**rol (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."
}