# Story Realization: Expanding Plot Events into Sentences

arXiv: https://arxiv.org/abs/1909.03480 · HTML: https://mlsanity.com/paper/1909.03480/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2019.

## Built on by (most cited first, 30 of 35)

- [Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionals](https://mlsanity.com/paper/2209.14958/) (2022)
- [Agents' Room: Narrative Generation through Multi-step Collaboration](https://mlsanity.com/paper/2410.02603/) (2024)
- [Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairs](https://mlsanity.com/paper/2101.10579/) (2021)
- [Wordcraft: a Human-AI Collaborative Editor for Story Writing](https://mlsanity.com/paper/2107.07430/) (2021)
- [Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning](https://mlsanity.com/paper/2105.01311/) (2021)
- [Commonsense Knowledge Aware Concept Selection For Diverse and Informative Visual Storytelling](https://mlsanity.com/paper/2102.02963/) (2021)
- [Collective Critics for Creative Story Generation](https://mlsanity.com/paper/2410.02428/) (2024)
- [Multimodal Event Transformer for Image-guided Story Ending Generation](https://mlsanity.com/paper/2301.11357/) (2023)
- [Learning Norms from Stories: A Prior for Value Aligned Agents](https://mlsanity.com/paper/1912.03553/) (2019)
- [GraphPlan: Story Generation by Planning with Event Graph](https://mlsanity.com/paper/2102.02977/) (2021)
- [LMMS Reloaded: Transformer-based Sense Embeddings for Disambiguation and Beyond](https://mlsanity.com/paper/2105.12449/) (2021)
- [Go Back in Time: Generating Flashbacks in Stories with Event Temporal Prompts](https://mlsanity.com/paper/2205.01898/) (2022)
- [Training Value-Aligned Reinforcement Learning Agents Using a Normative Prior](https://mlsanity.com/paper/2104.09469/) (2021)
- [Goal-Directed Story Generation: Augmenting Generative Language Models with Reinforcement Learning](https://mlsanity.com/paper/2112.08593/) (2021)
- [Telling Stories through Multi-User Dialogue by Modeling Character Relations](https://mlsanity.com/paper/2105.15054/) (2021)
- [Guiding Neural Story Generation with Reader Models](https://mlsanity.com/paper/2112.08596/) (2021)
- [TropeTwist: Trope-based Narrative Structure Generation](https://mlsanity.com/paper/2204.09672/) (2022)
- [WhatELSE: Shaping Narrative Spaces at Configurable Level of Abstraction for AI-bridged Interactive Storytelling](https://mlsanity.com/paper/2502.18641/) (2025)
- [Changing the Mind of Transformers for Topically-Controllable Language Generation](https://mlsanity.com/paper/2103.15335/) (2021)
- [CoRRPUS: Code-based Structured Prompting for Neurosymbolic Story Understanding](https://mlsanity.com/paper/2212.10754/) (2022)
- [StoryDB: Broad Multi-language Narrative Dataset](https://mlsanity.com/paper/2109.14396/) (2021)
- [Story Designer: Towards a Mixed-Initiative Tool to Create Narrative Structures](https://mlsanity.com/paper/2210.09294/) (2022)
- [A Temporal Variational Model for Story Generation](https://mlsanity.com/paper/2109.06807/) (2021)
- [NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization](https://mlsanity.com/paper/2212.01476/) (2022)
- [DYPLODOC: Dynamic Plots for Document Classification](https://mlsanity.com/paper/2107.12226/) (2021)
- [What is Wrong with Language Models that Can Not Tell a Story?](https://mlsanity.com/paper/2211.05044/) (2022)
- [From Words to Worlds: Transforming One-line Prompt into Immersive Multi-modal Digital Stories with Communicative LLM Agent](https://mlsanity.com/paper/2406.10478/) (2024)
- [Semantic Frame Forecast](https://mlsanity.com/paper/2104.05604/) (2021)
- [Automated Story Generation as Question-Answering](https://mlsanity.com/paper/2112.03808/) (2021)
- [Story Generation from Visual Inputs: Techniques, Related Tasks, and Challenges](https://mlsanity.com/paper/2406.02748/) (2024)

## Abstract (arXiv)

Neural network based approaches to automated story plot generation attempt to learn how to generate novel plots from a corpus of natural language plot summaries. Prior work has shown that a semantic abstraction of sentences called events improves neural plot generation and and allows one to decompose the problem into: (1) the generation of a sequence of events (event-to-event) and (2) the transformation of these events into natural language sentences (event-to-sentence). However, typical neural language generation approaches to event-to-sentence can ignore the event details and produce grammatically-correct but semantically-unrelated sentences. We present an ensemble-based model that generates natural language guided by events.We provide results---including a human subjects study---for a full end-to-end automated story generation system showing that our method generates more coherent and plausible stories than baseline approaches.