Once upon a Kernel: Extracting Important Events from Narratives

Abstract

Not all events in a narrative are created equal: some events are more important than others. Kernel events, a concept introduced in the field of narratology, are causally linked events that move the narrative forward, and cannot be removed without breaking the narrative’s logical coherence. While event detection and extraction tasks have been widely studied in natural language processing and information retrieval fields, the idea of kernel events has been largely unexplored. In this work, we introduce the first corpus and model for kernel event detection. Our contributions include: the refinement of the kernel event concept captured in detailed annotation guidelines grounded in narratological principles; an annotation study yielding a gold-standard dataset of kernel events in narrative texts; and a first-of-its-kind kernel event detection system. Annotation achieved an inter-annotator agreement of 0.61 Kappa, underscoring the reliability of the guidelines. Using these data, we trained several models in both fine-tuned and generative modes for kernel event detection, with a LoRA fine-tuned Llama3 achieving an F1 of 0.695. This work establishes a benchmark for kernel event detection, with potential applications in summarization, narrative similarity detection, and narrative understanding. We release our code and data for the benefit of other researchers.

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