# Plan, Write, and Revise: an Interactive System for Open-Domain Story Generation

arXiv: https://arxiv.org/abs/1904.02357 · HTML: https://mlsanity.com/paper/1904.02357/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2019.

## Built on by (most cited first, 30 of 43)

- [Re3: Generating Longer Stories With Recursive Reprompting and Revision](https://mlsanity.com/paper/2210.06774/) (2022)
- [RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text](https://mlsanity.com/paper/2305.13304/) (2023)
- [A Design Space for Intelligent and Interactive Writing Assistants](https://mlsanity.com/paper/2403.14117/) (2024)
- [Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairs](https://mlsanity.com/paper/2101.10579/) (2021)
- [Character-Centric Story Visualization via Visual Planning and Token Alignment](https://mlsanity.com/paper/2210.08465/) (2022)
- [Socially Aware Bias Measurements for Hindi Language Representations](https://mlsanity.com/paper/2110.07871/) (2021)
- [Zero-shot Sonnet Generation with Discourse-level Planning and Aesthetics Features](https://mlsanity.com/paper/2205.01821/) (2022)
- [Language Models can Self-Lengthen to Generate Long Texts](https://mlsanity.com/paper/2410.23933/) (2024)
- [Open-world Story Generation with Structured Knowledge Enhancement: A Comprehensive Survey](https://mlsanity.com/paper/2212.04634/) (2022)
- [Why is constrained neural language generation particularly challenging?](https://mlsanity.com/paper/2206.05395/) (2022)
- [GraphPlan: Story Generation by Planning with Event Graph](https://mlsanity.com/paper/2102.02977/) (2021)
- [Position: Towards Bidirectional Human-AI Alignment](https://mlsanity.com/paper/2406.09264/) (2024)
- [EventPlus: A Temporal Event Understanding Pipeline](https://mlsanity.com/paper/2101.04922/) (2021)
- [What Makes a Good Story and How Can We Measure It? A Comprehensive Survey of Story Evaluation](https://mlsanity.com/paper/2408.14622/) (2024)
- [Go Back in Time: Generating Flashbacks in Stories with Event Temporal Prompts](https://mlsanity.com/paper/2205.01898/) (2022)
- [Plot-guided Adversarial Example Construction for Evaluating Open-domain Story Generation](https://mlsanity.com/paper/2104.05801/) (2021)
- [Storyfier: Exploring Vocabulary Learning Support with Text Generation Models](https://mlsanity.com/paper/2308.03864/) (2023)
- [A Character-Centric Creative Story Generation via Imagination](https://mlsanity.com/paper/2409.16667/) (2024)
- [DiSCoL: Toward Engaging Dialogue Systems through Conversational Line Guided Response Generation](https://mlsanity.com/paper/2102.02191/) (2021)
- [Measuring Psychological Depth in Language Models](https://mlsanity.com/paper/2406.12680/) (2024)
- [Revisiting the Architectures like Pointer Networks to Efficiently Improve the Next Word Distribution, Summarization Factuality, and Beyond](https://mlsanity.com/paper/2305.12289/) (2023)
- [SWAG: Storytelling With Action Guidance](https://mlsanity.com/paper/2402.03483/) (2024)
- [Pap2Pat: Benchmarking Outline-Guided Long-Text Patent Generation with Patent-Paper Pairs](https://mlsanity.com/paper/2410.07009/) (2024)
- [Changing the Mind of Transformers for Topically-Controllable Language Generation](https://mlsanity.com/paper/2103.15335/) (2021)
- [CoRRPUS: Code-based Structured Prompting for Neurosymbolic Story Understanding](https://mlsanity.com/paper/2212.10754/) (2022)
- [End-to-end Story Plot Generator](https://mlsanity.com/paper/2310.08796/) (2023)
- [MAVEN-Arg: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation](https://mlsanity.com/paper/2311.09105/) (2023)
- [MAVEN-Fact: A Large-scale Event Factuality Detection Dataset](https://mlsanity.com/paper/2407.15352/) (2024)
- [A Temporal Variational Model for Story Generation](https://mlsanity.com/paper/2109.06807/) (2021)
- [PASTA: A Dataset for Modeling Participant States in Narratives](https://mlsanity.com/paper/2208.00329/) (2022)

## Abstract (arXiv)

Story composition is a challenging problem for machines and even for humans. We present a neural narrative generation system that interacts with humans to generate stories. Our system has different levels of human interaction, which enables us to understand at what stage of story-writing human collaboration is most productive, both to improving story quality and human engagement in the writing process. We compare different varieties of interaction in story-writing, story-planning, and diversity controls under time constraints, and show that increased types of human collaboration at both planning and writing stages results in a 10-50% improvement in story quality as compared to less interactive baselines. We also show an accompanying increase in user engagement and satisfaction with stories as compared to our own less interactive systems and to previous turn-taking approaches to interaction. Finally, we find that humans tasked with collaboratively improving a particular characteristic of a story are in fact able to do so, which has implications for future uses of human-in-the-loop systems.