# Stylized Story Generation with Style-Guided Planning

arXiv: https://arxiv.org/abs/2105.08625 · HTML: https://mlsanity.com/paper/2105.08625/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 35 references that are in the map.
- First posted on arXiv 18 May 2021.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories](https://mlsanity.com/paper/1604.01696/) (2016)
- [Toward Controlled Generation of Text](https://mlsanity.com/paper/1703.00955/) (2017)
- [Plan-And-Write: Towards Better Automatic Storytelling](https://mlsanity.com/paper/1811.05701/) (2018)
- [Strategies for Structuring Story Generation](https://mlsanity.com/paper/1902.01109/) (2019)
- [A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation](https://mlsanity.com/paper/2001.05139/) (2020)
- [Content Planning for Neural Story Generation with Aristotelian Rescoring](https://mlsanity.com/paper/2009.09870/) (2020)
- [MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models](https://mlsanity.com/paper/2010.00840/) (2020)
- [Target-Guided Open-Domain Conversation](https://mlsanity.com/paper/1905.11553/) (2019)
- [UNION: An Unreferenced Metric for Evaluating Open-ended Story Generation](https://mlsanity.com/paper/2009.07602/) (2020)
- [Polite Dialogue Generation Without Parallel Data](https://mlsanity.com/paper/1805.03162/) (2018)
- [A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation](https://mlsanity.com/paper/1808.06945/) (2018)
- [Hierarchically Structured Reinforcement Learning for Topically Coherent Visual Story Generation](https://mlsanity.com/paper/1805.08191/) (2018)
- [Practical and Ethical Considerations in the Effective use of Emotion and Sentiment Lexicons](https://mlsanity.com/paper/2011.03492/) (2020)
- [Plan ahead: Self-Supervised Text Planning for Paragraph Completion Task](https://mlsanity.com/paper/2010.05141/) (2020)
- [Guess who? Multilingual approach for the automated generation of author-stylized poetry](https://mlsanity.com/paper/1807.07147/) (2018)
- [Large Scale Multi-Actor Generative Dialog Modeling](https://mlsanity.com/paper/2005.06114/) (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)
- [PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation](https://mlsanity.com/paper/2203.09100/) (2022)
- [LOT: A Story-Centric Benchmark for Evaluating Chinese Long Text Understanding and Generation](https://mlsanity.com/paper/2108.12960/) (2021)
- [TimeArena: Shaping Efficient Multitasking Language Agents in a Time-Aware Simulation](https://mlsanity.com/paper/2402.05733/) (2024)
- [Collective Critics for Creative Story Generation](https://mlsanity.com/paper/2410.02428/) (2024)
- [EtriCA: Event-Triggered Context-Aware Story Generation Augmented by Cross Attention](https://mlsanity.com/paper/2210.12463/) (2022)
- [What Makes a Good Story and How Can We Measure It? A Comprehensive Survey of Story Evaluation](https://mlsanity.com/paper/2408.14622/) (2024)
- [A Corpus for Understanding and Generating Moral Stories](https://mlsanity.com/paper/2204.09438/) (2022)
- [MPCODER: Multi-user Personalized Code Generator with Explicit and Implicit Style Representation Learning](https://mlsanity.com/paper/2406.17255/) (2024)
- [Stylized Data-to-Text Generation: A Case Study in the E-Commerce Domain](https://mlsanity.com/paper/2305.03256/) (2023)
- [SARD: A Human-AI Collaborative Story Generation](https://mlsanity.com/paper/2403.01575/) (2024)
- [Frankentext: Stitching random text fragments into long-form narratives](https://mlsanity.com/paper/2505.18128/) (2025)
- [CHAE: Fine-Grained Controllable Story Generation with Characters, Actions and Emotions](https://mlsanity.com/paper/2210.05221/) (2022)
- [Integrating AI Planning with Natural Language Processing: A Combination of Explicit and Tacit Knowledge](https://mlsanity.com/paper/2202.07138/) (2022)
- [Text-Based Action-Model Acquisition for Planning](https://mlsanity.com/paper/2202.08373/) (2022)
- [Psychology-guided Controllable Story Generation](https://mlsanity.com/paper/2210.07493/) (2022)
- [ATLAS: Improving Lay Summarisation with Attribute-based Control](https://mlsanity.com/paper/2406.05625/) (2024)
- [Schema-Guided Culture-Aware Complex Event Simulation with Multi-Agent Role-Play](https://mlsanity.com/paper/2410.18935/) (2024)
- [Dependency-based Mixture Language Models](https://mlsanity.com/paper/2203.10256/) (2022)
- [LLMs Behind the Scenes: Enabling Narrative Scene Illustration](https://mlsanity.com/paper/2509.22940/) (2025)
- [A Benchmark for Understanding and Generating Dialogue between Characters in Stories](https://mlsanity.com/paper/2209.08524/) (2022)

## Abstract (arXiv)

Current storytelling systems focus more ongenerating stories with coherent plots regard-less of the narration style, which is impor-tant for controllable text generation. There-fore, we propose a new task, stylized story gen-eration, namely generating stories with speci-fied style given a leading context. To tacklethe problem, we propose a novel generationmodel that first plans the stylized keywordsand then generates the whole story with theguidance of the keywords. Besides, we pro-pose two automatic metrics to evaluate theconsistency between the generated story andthe specified style. Experiments demonstratesthat our model can controllably generateemo-tion-driven orevent-driven stories based onthe ROCStories dataset (Mostafazadeh et al.,2016). Our study presents insights for stylizedstory generation in further research.