# Guiding Neural Story Generation with Reader Models

arXiv: https://arxiv.org/abs/2112.08596 · HTML: https://mlsanity.com/paper/2112.08596/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 69 references that are in the map.
- First posted on arXiv 16 Dec 2021.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [The Woman Worked as a Babysitter: On Biases in Language Generation](https://mlsanity.com/paper/1909.01326/) (2019)
- [Texygen: A Benchmarking Platform for Text Generation Models](https://mlsanity.com/paper/1802.01886/) (2018)
- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019)
- [COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs](https://mlsanity.com/paper/2010.05953/) (2020)
- [End-to-end Neural Coreference Resolution](https://mlsanity.com/paper/1707.07045/) (2017)
- [Plan-And-Write: Towards Better Automatic Storytelling](https://mlsanity.com/paper/1811.05701/) (2018)
- [Simple BERT Models for Relation Extraction and Semantic Role Labeling](https://mlsanity.com/paper/1904.05255/) (2019)
- [Strategies for Structuring Story Generation](https://mlsanity.com/paper/1902.01109/) (2019)
- [PlotMachines: Outline-Conditioned Generation with Dynamic Plot State Tracking](https://mlsanity.com/paper/2004.14967/) (2020)
- [Content Planning for Neural Story Generation with Aristotelian Rescoring](https://mlsanity.com/paper/2009.09870/) (2020)
- [Event Representations for Automated Story Generation with Deep Neural Nets](https://mlsanity.com/paper/1706.01331/) (2017)
- [Narrative Planning: Balancing Plot and Character](https://mlsanity.com/paper/1401.3841/) (2014)
- [Story Ending Generation with Incremental Encoding and Commonsense Knowledge](https://mlsanity.com/paper/1808.10113/) (2018)
- [Knowledgeable Reader: Enhancing Cloze-Style Reading Comprehension with External Commonsense Knowledge](https://mlsanity.com/paper/1805.07858/) (2018)
- [Automated Storytelling via Causal, Commonsense Plot Ordering](https://mlsanity.com/paper/2009.00829/) (2020)
- [Story Realization: Expanding Plot Events into Sentences](https://mlsanity.com/paper/1909.03480/) (2019)
- [Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning](https://mlsanity.com/paper/2105.01311/) (2021)
- [Bringing Stories Alive: Generating Interactive Fiction Worlds](https://mlsanity.com/paper/2001.10161/) (2020)
- [DeepTingle](https://mlsanity.com/paper/1705.03557/) (2017)

## Built on by (most cited first, 12 of 12)

- [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)
- [Improving Your Model Ranking on Chatbot Arena by Vote Rigging](https://mlsanity.com/paper/2501.17858/) (2025)
- [ComFact: A Benchmark for Linking Contextual Commonsense Knowledge](https://mlsanity.com/paper/2210.12678/) (2022)
- [STORYTELLER: An Enhanced Plot-Planning Framework for Coherent and Cohesive Story Generation](https://mlsanity.com/paper/2506.02347/) (2025)
- [CHIRON: Rich Character Representations in Long-Form Narratives](https://mlsanity.com/paper/2406.10190/) (2024)
- [Story Shaping: Teaching Agents Human-like Behavior with Stories](https://mlsanity.com/paper/2301.10107/) (2023)
- [Sequentially Controlled Text Generation](https://mlsanity.com/paper/2301.02299/) (2023)
- [Ambient Adventures: Teaching ChatGPT on Developing Complex Stories](https://mlsanity.com/paper/2308.01734/) (2023)
- [Schema-Guided Culture-Aware Complex Event Simulation with Multi-Agent Role-Play](https://mlsanity.com/paper/2410.18935/) (2024)
- [Regional Tiny Stories: Using Small Models to Compare Language Learning and Tokenizer Performance](https://mlsanity.com/paper/2504.07989/) (2025)
- [Microsaccade-Inspired Probing: Positional Encoding Perturbations Reveal LLM Misbehaviours](https://mlsanity.com/paper/2510.01288/) (2025)

## Abstract (arXiv)

Automated storytelling has long captured the attention of researchers for the ubiquity of narratives in everyday life. However, it is challenging to maintain coherence and stay on-topic toward a specific ending when generating narratives with neural language models. In this paper, we introduce Story generation with Reader Models (StoRM), a framework in which a reader model is used to reason about the story should progress. A reader model infers what a human reader believes about the concepts, entities, and relations about the fictional story world. We show how an explicit reader model represented as a knowledge graph affords story coherence and provides controllability in the form of achieving a given story world state goal. Experiments show that our model produces significantly more coherent and on-topic stories, outperforming baselines in dimensions including plot plausibility and staying on topic.