# GraphPlan: Story Generation by Planning with Event Graph

arXiv: https://arxiv.org/abs/2102.02977 · HTML: https://mlsanity.com/paper/2102.02977/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 37 references that are in the map.
- First posted on arXiv 5 Feb 2021.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018)
- [Plan-And-Write: Towards Better Automatic Storytelling](https://mlsanity.com/paper/1811.05701/) (2018)
- [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)
- [Controllable Neural Story Plot Generation via Reward Shaping](https://mlsanity.com/paper/1809.10736/) (2018)
- [Plan, Write, and Revise: an Interactive System for Open-Domain Story Generation](https://mlsanity.com/paper/1904.02357/) (2019)
- [A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation](https://mlsanity.com/paper/1808.06945/) (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)
- [Progressive Generation of Long Text with Pretrained Language Models](https://mlsanity.com/paper/2006.15720/) (2020)
- [Two Discourse Driven Language Models for Semantics](https://mlsanity.com/paper/1606.05679/) (2016)
- [Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model](https://mlsanity.com/paper/1906.01231/) (2019)
- [Discriminative Sentence Modeling for Story Ending Prediction](https://mlsanity.com/paper/1912.09008/) (2019)

## Built on by (most cited first, 18 of 18)

- [EtriCA: Event-Triggered Context-Aware Story Generation Augmented by Cross Attention](https://mlsanity.com/paper/2210.12463/) (2022)
- [NGEP: A Graph-based Event Planning Framework for Story Generation](https://mlsanity.com/paper/2210.10602/) (2022)
- [LongStory: Coherent, Complete and Length Controlled Long story Generation](https://mlsanity.com/paper/2311.15208/) (2023)
- [Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering](https://mlsanity.com/paper/2203.07633/) (2022)
- [Advancing Precise Outline-Conditioned Text Generation with Task Duality and Explicit Outline Control](https://mlsanity.com/paper/2305.14459/) (2023)
- [A Cross-Attention Augmented Model for Event-Triggered Context-Aware Story Generation](https://mlsanity.com/paper/2311.11271/) (2023)
- [On the Automatic Generation and Simplification of Children's Stories](https://mlsanity.com/paper/2310.18502/) (2023)
- [Train & Constrain: Phonologically Informed Tongue-Twister Generation from Topics and Paraphrases](https://mlsanity.com/paper/2403.13901/) (2024)
- [Sentence-level Media Bias Analysis with Event Relation Graph](https://mlsanity.com/paper/2404.01722/) (2024)
- [COMET-M: Reasoning about Multiple Events in Complex Sentences](https://mlsanity.com/paper/2305.14617/) (2023)
- [Schema-Guided Culture-Aware Complex Event Simulation with Multi-Agent Role-Play](https://mlsanity.com/paper/2410.18935/) (2024)
- [STAR: A Foundation Model-driven Framework for Robust Task Planning and Failure Recovery in Robotic Systems](https://mlsanity.com/paper/2503.06060/) (2025)
- [Multi-document Summarization through Multi-document Event Relation Graph Reasoning in LLMs: a case study in Framing Bias Mitigation](https://mlsanity.com/paper/2506.12978/) (2025)
- [Character-level Chinese Backpack Language Models](https://mlsanity.com/paper/2310.12751/) (2023)
- [Human-AI Narrative Synthesis to Foster Shared Understanding in Civic Decision-Making](https://mlsanity.com/paper/2509.19643/) (2025)
- [Graph-based Keyword Planning for Legal Clause Generation from Topics](https://mlsanity.com/paper/2301.06901/) (2023)
- [PromptCL: Improving Event Representation via Prompt Template and Contrastive Learning](https://mlsanity.com/paper/2404.17877/) (2024)
- [ConVAWG: A Retrieval-Grounded Framework for Controlled Synthetic Dialogue Generation in Violence Against Women and Girls](https://mlsanity.com/paper/2608.11200/) (2026)

## Abstract (arXiv)

Story generation is a task that aims to automatically produce multiple sentences to make up a meaningful story. This task is challenging because it requires high-level understanding of semantic meaning of sentences and causality of story events. Naive sequence-to-sequence models generally fail to acquire such knowledge, as the logical correctness can hardly be guaranteed in a text generation model without the strategic planning. In this paper, we focus on planning a sequence of events assisted by event graphs, and use the events to guide the generator. Instead of using a sequence-to-sequence model to output a storyline as in some existing works, we propose to generate an event sequence by walking on an event graph. The event graphs are built automatically based on the corpus. To evaluate the proposed approach, we conduct human evaluation both on event planning and story generation. Based on large-scale human annotation results, our proposed approach is shown to produce more logically correct event sequences and stories.