# A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation

arXiv: https://arxiv.org/abs/1808.06945 · HTML: https://mlsanity.com/paper/1808.06945/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2018.

## Built on by (most cited first, 30 of 42)

- [Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning](https://mlsanity.com/paper/2107.02794/) (2021)
- [Of Human Criteria and Automatic Metrics: A Benchmark of the Evaluation of Story Generation](https://mlsanity.com/paper/2208.11646/) (2022)
- [ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification](https://mlsanity.com/paper/2203.02225/) (2022)
- [Learning Personalized Alignment for Evaluating Open-ended Text Generation](https://mlsanity.com/paper/2310.03304/) (2023)
- [Stylized Story Generation with Style-Guided Planning](https://mlsanity.com/paper/2105.08625/) (2021)
- [GraphPlan: Story Generation by Planning with Event Graph](https://mlsanity.com/paper/2102.02977/) (2021)
- [Go Back in Time: Generating Flashbacks in Stories with Event Temporal Prompts](https://mlsanity.com/paper/2205.01898/) (2022)
- [VIST-GPT: Ushering in the Era of Visual Storytelling with LLMs?](https://mlsanity.com/paper/2504.19267/) (2025)
- [Ex3: Automatic Novel Writing by Extracting, Excelsior and Expanding](https://mlsanity.com/paper/2408.08506/) (2024)
- [TALES: A Taxonomy and Analysis of Cultural Representations in LLM-generated Stories](https://mlsanity.com/paper/2511.21322/) (2025)
- [Sketch and Customize: A Counterfactual Story Generator](https://mlsanity.com/paper/2104.00929/) (2021)
- [Unsupervised Editing for Counterfactual Stories](https://mlsanity.com/paper/2112.05417/) (2021)
- [Combining Hierachical VAEs with LLMs for clinically meaningful timeline summarisation in social media](https://mlsanity.com/paper/2401.16240/) (2024)
- [SWAG: Storytelling With Action Guidance](https://mlsanity.com/paper/2402.03483/) (2024)
- [Changing the Mind of Transformers for Topically-Controllable Language Generation](https://mlsanity.com/paper/2103.15335/) (2021)
- [Towards Automatic Composition of ASP Programs from Natural Language Specifications](https://mlsanity.com/paper/2403.04541/) (2024)
- [StoryDB: Broad Multi-language Narrative Dataset](https://mlsanity.com/paper/2109.14396/) (2021)
- [Incorporating Commonsense Knowledge into Story Ending Generation via Heterogeneous Graph Networks](https://mlsanity.com/paper/2201.12538/) (2022)
- [A Temporal Variational Model for Story Generation](https://mlsanity.com/paper/2109.06807/) (2021)
- [Every picture tells a story: Image-grounded controllable stylistic story generation](https://mlsanity.com/paper/2209.01638/) (2022)
- [Sequentially Controlled Text Generation](https://mlsanity.com/paper/2301.02299/) (2023)
- [STORYWARS: A Dataset and Instruction Tuning Baselines for Collaborative Story Understanding and Generation](https://mlsanity.com/paper/2305.08152/) (2023)
- [Advancing Precise Outline-Conditioned Text Generation with Task Duality and Explicit Outline Control](https://mlsanity.com/paper/2305.14459/) (2023)
- [Visual Storytelling with Question-Answer Plans](https://mlsanity.com/paper/2310.05295/) (2023)
- [A Novel Computational and Modeling Foundation for Automatic Coherence Assessment](https://mlsanity.com/paper/2310.00598/) (2023)
- [Integrating AI Planning with Natural Language Processing: A Combination of Explicit and Tacit Knowledge](https://mlsanity.com/paper/2202.07138/) (2022)
- [Grounded Keys-to-Text Generation: Towards Factual Open-Ended Generation](https://mlsanity.com/paper/2212.01956/) (2022)
- [InFillmore: Frame-Guided Language Generation with Bidirectional Context](https://mlsanity.com/paper/2103.04941/) (2021)
- [TopNet: Learning from Neural Topic Model to Generate Long Stories](https://mlsanity.com/paper/2112.07259/) (2021)
- [Text-Based Action-Model Acquisition for Planning](https://mlsanity.com/paper/2202.08373/) (2022)

## Abstract (arXiv)

Narrative story generation is a challenging problem because it demands the generated sentences with tight semantic connections, which has not been well studied by most existing generative models. To address this problem, we propose a skeleton-based model to promote the coherence of generated stories. Different from traditional models that generate a complete sentence at a stroke, the proposed model first generates the most critical phrases, called skeleton, and then expands the skeleton to a complete and fluent sentence. The skeleton is not manually defined, but learned by a reinforcement learning method. Compared to the state-of-the-art models, our skeleton-based model can generate significantly more coherent text according to human evaluation and automatic evaluation. The G-score is improved by 20.1% in the human evaluation. The code is available at https://github.com/lancopku/Skeleton-Based-Generation-Model