Taking the Derivative of a Story: A Novel Approach to Fiction Scene Segmentation

Research Summary

This paper presents a novel method for identifying scene transitions in fiction by treating the narrative as a sequence of sentence embeddings. We calculate a “derivative” by measuring changes between adjacent embeddings, then use local minima in the smoothed derivative signal as potential scene boundaries. A neural classifier is trained on annotated scenes to filter out false positives.

Motivation

Scene segmentation in fiction is a challenging NLP task with low inter-annotator agreement. Existing models often rely on classification or co-reference features, but struggle with generalization. Our approach reframes the task as a structural problem, leveraging embedding dynamics to detect changes in narrative momentum.

Approach

• Embedding Derivative: Compute L2 differences between adjacent sentence embeddings.
• Smoothing: Apply Gaussian smoothing to extract trends.
• Minima Detection: Use local minima as candidate scene transitions.
• Classification: Train a neural network to classify these candidates using surrounding context.
• Optional GPT-4 Check: Use prompting to pinpoint scene boundaries within selected text spans.

Results

• Achieves state-of-the-art accuracy on a 32-story dataset with diverse genres and lengths.
• Outperforms prior baselines on F1 and Pointwise Dissimilarity metrics.
• Shows robust performance across amateur and professional writing, though novel-length texts introduce more variance.

Research Team

• Michael DeBuse, Caelen Miller, Caleb Bradshaw, Abel Palmer
• Dr. Sean Warnick: Faculty advisor

Notes

This paper is currently under review at Transactions of the Association for Computational Linguistics (TACL). A preprint is not available for distribution at this time.