Improving Pacing in Long-Form Story Planning
Submission Type: Regular Short Paper
Submission Track: Natural Language Generation
Submission Track 2: NLP Applications
Keywords: Story Generation, Pacing, Hierarchical Planning
TL;DR: We propose a concrete outline control (CONCOCT) framework to improve pacing consistency in hierarchical story outlines.
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.
Submission Number: 5156
Loading