@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://aclanthology.org/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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Improving Pacing in Long-Form Story Planning
%A Wang, Yichen
%A Yang, Kevin
%A Liu, Xiaoming
%A Klein, Dan
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F wang-etal-2023-improving-pacing
%X 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.
%R 10.18653/v1/2023.findings-emnlp.723
%U https://aclanthology.org/2023.findings-emnlp.723
%U https://doi.org/10.18653/v1/2023.findings-emnlp.723
%P 10788-10845
Markdown (Informal)
[Improving Pacing in Long-Form Story Planning](https://aclanthology.org/2023.findings-emnlp.723) (Wang et al., Findings 2023)
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.