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.- Anthology ID:
- 2023.findings-emnlp.723
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 10788–10845
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2023.findings-emnlp.723/
- DOI:
- 10.18653/v1/2023.findings-emnlp.723
- Cite (ACL):
- Yichen Wang, Kevin Yang, Xiaoming Liu, and Dan Klein. 2023. Improving Pacing in Long-Form Story Planning. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 10788–10845, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Improving Pacing in Long-Form Story Planning (Wang et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2023.findings-emnlp.723.pdf