# Learning to Predict Explainable Plots for Neural Story Generation

arXiv: https://arxiv.org/abs/1912.02395 · HTML: https://mlsanity.com/paper/1912.02395/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2019.

## Built on by (most cited first, 2 of 2)

- [Changing the Mind of Transformers for Topically-Controllable Language Generation](https://mlsanity.com/paper/2103.15335/) (2021)
- [InFillmore: Frame-Guided Language Generation with Bidirectional Context](https://mlsanity.com/paper/2103.04941/) (2021)

## Abstract (arXiv)

Story generation is an important natural language processing task that aims to generate coherent stories automatically. While the use of neural networks has proven effective in improving story generation, how to learn to generate an explainable high-level plot still remains a major challenge. In this work, we propose a latent variable model for neural story generation. The model treats an outline, which is a natural language sentence explainable to humans, as a latent variable to represent a high-level plot that bridges the input and output. We adopt an external summarization model to guide the latent variable model to learn how to generate outlines from training data. Experiments show that our approach achieves significant improvements over state-of-the-art methods in both automatic and human evaluations.