# Hierarchical Neural Story Generation

arXiv: https://arxiv.org/abs/1805.04833 · HTML: https://mlsanity.com/paper/1805.04833/

## Key facts

- Cited by 1,193 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2018.

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## Abstract (arXiv)

We explore story generation: creative systems that can build coherent and fluent passages of text about a topic. We collect a large dataset of 300K human-written stories paired with writing prompts from an online forum. Our dataset enables hierarchical story generation, where the model first generates a premise, and then transforms it into a passage of text. We gain further improvements with a novel form of model fusion that improves the relevance of the story to the prompt, and adding a new gated multi-scale self-attention mechanism to model long-range context. Experiments show large improvements over strong baselines on both automated and human evaluations. Human judges prefer stories generated by our approach to those from a strong non-hierarchical model by a factor of two to one.