# Improving Pacing in Long-Form Story Planning

arXiv: https://arxiv.org/abs/2311.04459 · HTML: https://mlsanity.com/paper/2311.04459/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 8 Nov 2023.

## Builds on (most cited first, 12 of 12)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Unsupervised Dense Information Retrieval with Contrastive Learning](https://mlsanity.com/paper/2112.09118/) (2021)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks](https://mlsanity.com/paper/2204.07705/) (2022)
- [BookSum: A Collection of Datasets for Long-form Narrative Summarization](https://mlsanity.com/paper/2105.08209/) (2021)
- [RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text](https://mlsanity.com/paper/2305.13304/) (2023)
- [Controlling Output Length in Neural Encoder-Decoders](https://mlsanity.com/paper/1609.09552/) (2016)
- [DOC: Improving Long Story Coherence With Detailed Outline Control](https://mlsanity.com/paper/2212.10077/) (2022)
- [Summarization with Precise Length Control](https://mlsanity.com/paper/2305.05171/) (2023)
- [Interpretable Multi-Headed Attention for Abstractive Summarization at Controllable Lengths](https://mlsanity.com/paper/2002.07845/) (2020)

## Built on by (most cited first, 21 of 21)

- [Agents' Room: Narrative Generation through Multi-step Collaboration](https://mlsanity.com/paper/2410.02603/) (2024)
- [Generating Long-form Story Using Dynamic Hierarchical Outlining with Memory-Enhancement](https://mlsanity.com/paper/2412.13575/) (2024)
- [DataNarrative: Automated Data-Driven Storytelling with Visualizations and Texts](https://mlsanity.com/paper/2408.05346/) (2024)
- [Collective Critics for Creative Story Generation](https://mlsanity.com/paper/2410.02428/) (2024)
- [StoryWriter: A Multi-Agent Framework for Long Story Generation](https://mlsanity.com/paper/2506.16445/) (2025)
- [What Makes a Good Story and How Can We Measure It? A Comprehensive Survey of Story Evaluation](https://mlsanity.com/paper/2408.14622/) (2024)
- [MoPS: Modular Story Premise Synthesis for Open-Ended Automatic Story Generation](https://mlsanity.com/paper/2406.05690/) (2024)
- [FACTTRACK: Time-Aware World State Tracking in Story Outlines](https://mlsanity.com/paper/2407.16347/) (2024)
- [Natural Language Generation for Visualizations: State of the Art, Challenges and Future Directions](https://mlsanity.com/paper/2409.19747/) (2024)
- [Lost in Stories: Consistency Bugs in Long Story Generation by LLMs](https://mlsanity.com/paper/2603.05890/) (2026)
- [SkyScript-100M: 1,000,000,000 Pairs of Scripts and Shooting Scripts for Short Drama](https://mlsanity.com/paper/2408.09333/) (2024)
- [MLD-EA: Check and Complete Narrative Coherence by Introducing Emotions and Actions](https://mlsanity.com/paper/2412.02897/) (2024)
- [TokenSwift: Lossless Acceleration of Ultra Long Sequence Generation](https://mlsanity.com/paper/2502.18890/) (2025)
- [StorySage: Conversational Autobiography Writing Powered by a Multi-Agent Framework](https://mlsanity.com/paper/2506.14159/) (2025)
- [StoryBox: Collaborative Multi-Agent Simulation for Hybrid Bottom-Up Long-Form Story Generation Using Large Language Models](https://mlsanity.com/paper/2510.11618/) (2025)
- [Vidmento: Creating Video Stories Through Context-Aware Expansion With Generative Video](https://mlsanity.com/paper/2601.22013/) (2026)
- [Pastiche Novel Generation Creating: Fan Fiction You Love in Your Favorite Author's Style](https://mlsanity.com/paper/2502.15616/) (2025)
- [Beyond Direct Generation: A Decomposed Approach to Well-Crafted Screenwriting with LLMs](https://mlsanity.com/paper/2510.23163/) (2025)
- [NARRA-Gym for Evaluating Interactive Narrative Agents](https://mlsanity.com/paper/2605.08503/) (2026)
- [Measuring Information Distortion in Hierarchical Ultra long Novel Reconstruction:The Optimal Expansion Ratio](https://mlsanity.com/paper/2505.12572/) (2025)
- [Scaling Long-Form Story Generation via Narrative State Tracking](https://mlsanity.com/paper/2609.35759/) (2026)

## Abstract (arXiv)

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.