# Automated Storytelling via Causal, Commonsense Plot Ordering

arXiv: https://arxiv.org/abs/2009.00829 · HTML: https://mlsanity.com/paper/2009.00829/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2020.

## Built on by (most cited first, 30 of 38)

- [Symbolic Knowledge Distillation: from General Language Models to Commonsense Models](https://mlsanity.com/paper/2110.07178/) (2021)
- [The Next Chapter: A Study of Large Language Models in Storytelling](https://mlsanity.com/paper/2301.09790/) (2023)
- [It's not Rocket Science : Interpreting Figurative Language in Narratives](https://mlsanity.com/paper/2109.00087/) (2021)
- [Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning](https://mlsanity.com/paper/2105.01311/) (2021)
- [Automatic Story Generation: Challenges and Attempts](https://mlsanity.com/paper/2102.12634/) (2021)
- [Open-world Story Generation with Structured Knowledge Enhancement: A Comprehensive Survey](https://mlsanity.com/paper/2212.04634/) (2022)
- [Creating Suspenseful Stories: Iterative Planning with Large Language Models](https://mlsanity.com/paper/2402.17119/) (2024)
- [ReelFramer: Human-AI Co-Creation for News-to-Video Translation](https://mlsanity.com/paper/2304.09653/) (2023)
- [GraphPlan: Story Generation by Planning with Event Graph](https://mlsanity.com/paper/2102.02977/) (2021)
- [Persona-Guided Planning for Controlling the Protagonist's Persona in Story Generation](https://mlsanity.com/paper/2204.10703/) (2022)
- [Go Back in Time: Generating Flashbacks in Stories with Event Temporal Prompts](https://mlsanity.com/paper/2205.01898/) (2022)
- [Goal-Directed Story Generation: Augmenting Generative Language Models with Reinforcement Learning](https://mlsanity.com/paper/2112.08593/) (2021)
- [Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning](https://mlsanity.com/paper/2210.07792/) (2022)
- [GROVE: A Retrieval-augmented Complex Story Generation Framework with A Forest of Evidence](https://mlsanity.com/paper/2310.05388/) (2023)
- [Telling Stories through Multi-User Dialogue by Modeling Character Relations](https://mlsanity.com/paper/2105.15054/) (2021)
- [GD-COMET: A Geo-Diverse Commonsense Inference Model](https://mlsanity.com/paper/2310.15383/) (2023)
- [COINS: Dynamically Generating COntextualized Inference Rules for Narrative Story Completion](https://mlsanity.com/paper/2106.02497/) (2021)
- [Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form Stories](https://mlsanity.com/paper/2109.03754/) (2021)
- [Guiding Neural Story Generation with Reader Models](https://mlsanity.com/paper/2112.08596/) (2021)
- [Failure Modes of LLMs for Causal Reasoning on Narratives](https://mlsanity.com/paper/2410.23884/) (2024)
- [LaMPP: Language Models as Probabilistic Priors for Perception and Action](https://mlsanity.com/paper/2302.02801/) (2023)
- [Event Causality Is Key to Computational Story Understanding](https://mlsanity.com/paper/2311.09648/) (2023)
- [Do Language Models Perform Generalizable Commonsense Inference?](https://mlsanity.com/paper/2106.11533/) (2021)
- [What Makes the Story Forward? Inferring Commonsense Explanations as Prompts for Future Event Generation](https://mlsanity.com/paper/2201.07099/) (2022)
- [Fabula Entropy Indexing: Objective Measures of Story Coherence](https://mlsanity.com/paper/2104.07472/) (2021)
- [Generating Coherent Narratives by Learning Dynamic and Discrete Entity States with a Contrastive Framework](https://mlsanity.com/paper/2208.03985/) (2022)
- [Neural Story Planning](https://mlsanity.com/paper/2212.08718/) (2022)
- [COMMA: Modeling Relationship among Motivations, Emotions and Actions in Language-based Human Activities](https://mlsanity.com/paper/2209.06470/) (2022)
- [kogito: A Commonsense Knowledge Inference Toolkit](https://mlsanity.com/paper/2211.08451/) (2022)
- [Narrix: Remixing Narrative Strategies from Examples for Story Writing](https://mlsanity.com/paper/2604.07643/) (2026)

## Abstract (arXiv)

Automated story plot generation is the task of generating a coherent sequence of plot events. Causal relations between plot events are believed to increase the perception of story and plot coherence. In this work, we introduce the concept of soft causal relations as causal relations inferred from commonsense reasoning. We demonstrate C2PO, an approach to narrative generation that operationalizes this concept through Causal, Commonsense Plot Ordering. Using human-participant protocols, we evaluate our system against baseline systems with different commonsense reasoning reasoning and inductive biases to determine the role of soft causal relations in perceived story quality. Through these studies we also probe the interplay of how changes in commonsense norms across storytelling genres affect perceptions of story quality.