Story Generation: LSTM vs GRU
Comparative sequence modeling for outline-to-story generation.
Research pipeline comparing LSTM and GRU models for interactive, seed-word story generation on classic short stories.
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6 technologies
Gallery
2 screenshots
Source
Open on GitHub
Problem
For outline-to-story generation, teams need evidence on which recurrent architecture balances coherence, creativity, and training efficiency before investing in production NLP pipelines.
Solution
Designed a comparative research pipeline that preprocesses classic short-story corpora, trains bidirectional LSTM and GRU models on shared outline-to-story tasks, and evaluates outputs on coherence, creativity, and convergence behavior.
Highlights
- Controlled comparison between LSTM and GRU under identical datasets
- Bidirectional LSTM with 256 hidden units and interactive seed-word prediction
- Evaluation focused on quality, creativity, and training efficiency
Outcomes
- 79% accuracy based on human evaluation (per project README)
- Reproducible baseline for sequence-model selection
- Documented trade-offs useful for future generative text systems
Repository
Story Generation repository
LSTM and GRU models for interactive story generation with configurable training and human-evaluated outputs.
Human evaluation
79% accuracy
LSTM architecture
Bidirectional, 256 hidden units
Dataset
Classic short stories (ClassicShorts)
Training scope
100,000 instances per README
- LSTM and GRU implementations for story generation
- Interactive prediction with user-provided seed words and context
- Configurable sequence length and context vectors
- Jupyter notebooks for training and evaluation workflows
- Flask app for interactive story generation
Stack