# Modelling Suspense in Short Stories as Uncertainty Reduction over Neural Representation

arXiv: https://arxiv.org/abs/2004.14905 · HTML: https://mlsanity.com/paper/2004.14905/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2020.

## Built on by (most cited first, 14 of 14)

- [Creating Suspenseful Stories: Iterative Planning with Large Language Models](https://mlsanity.com/paper/2402.17119/) (2024)
- [Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form Stories](https://mlsanity.com/paper/2109.03754/) (2021)
- [Affective and Dynamic Beam Search for Story Generation](https://mlsanity.com/paper/2310.15079/) (2023)
- [A Temporal Variational Model for Story Generation](https://mlsanity.com/paper/2109.06807/) (2021)
- [Topical Segmentation of Spoken Narratives: A Test Case on Holocaust Survivor Testimonies](https://mlsanity.com/paper/2210.13783/) (2022)
- [LLMs Exhibit Significantly Lower Uncertainty in Creative Writing Than Professional Writers](https://mlsanity.com/paper/2602.16162/) (2026)
- [How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements](https://mlsanity.com/paper/2106.03048/) (2021)
- [Prosody Analysis of Audiobooks](https://mlsanity.com/paper/2310.06930/) (2023)
- [Multi-Agent System for AI-Assisted Extraction of Narrative Arcs in TV Series](https://mlsanity.com/paper/2503.04817/) (2025)
- [Narrative Memory in Machines: Multi-Agent Arc Extraction in Serialized TV](https://mlsanity.com/paper/2508.07010/) (2025)
- [Do Language Models Agree with Human Perceptions of Suspense in Stories?](https://mlsanity.com/paper/2508.15794/) (2025)
- [Spoiler Alert: Narrative Forecasting as a Metric for Tension in LLM Storytelling](https://mlsanity.com/paper/2604.09854/) (2026)
- [The Challenge and Reward of Fair Play in Narrative: A Computational Approach](https://mlsanity.com/paper/2507.13841/) (2025)
- [An Interpretable Linguistically-Grounded Analysis of Scene Saliency in Movie Screenplays](https://mlsanity.com/paper/2610.15544/) (2026)

## Abstract (arXiv)

Suspense is a crucial ingredient of narrative fiction, engaging readers and making stories compelling. While there is a vast theoretical literature on suspense, it is computationally not well understood. We compare two ways for modelling suspense: surprise, a backward-looking measure of how unexpected the current state is given the story so far; and uncertainty reduction, a forward-looking measure of how unexpected the continuation of the story is. Both can be computed either directly over story representations or over their probability distributions. We propose a hierarchical language model that encodes stories and computes surprise and uncertainty reduction. Evaluating against short stories annotated with human suspense judgements, we find that uncertainty reduction over representations is the best predictor, resulting in near-human accuracy. We also show that uncertainty reduction can be used to predict suspenseful events in movie synopses.