SemEval-2026 Task 4: Narrative Story Similarity and Narrative Representation Learning

TL;DR

This paper introduces a shared task on narrative similarity and representation learning, providing a new dataset and evaluating various systems, highlighting the potential for future improvements in automated narrative analysis.

Contribution

It defines a novel concept of narrative similarity, creates a dataset with annotated story triples, and evaluates multiple systems, including ensemble and embedding-based approaches.

Findings

LLM ensembles achieved top scores in classification.

Pre- and post-processed pretrained embeddings performed comparably to fine-tuned models.

Potential exists for improving automated narrative similarity systems.

Abstract

We present the shared task on narrative similarity and narrative representation learning - NSNRL (pronounced "nass-na-rel"). The task operationalizes narrative similarity as a binary classification problem: determining which of two stories is more similar to an anchor story. We introduce a novel definition of narrative similarity, compatible with both narrative theory and intuitive judgment. Based on the similarity judgments collected under this concept, we also evaluate narrative embedding representations. We collected at least two annotations each for more than 1,000 story summary triples, with each annotation being backed by at least two annotators in agreement. This paper describes the sampling and annotation process for the dataset; further, we give an overview of the submitted systems and the techniques they employ. We received a total of 71 final submissions from 46 teams across our two tracks. In our triple-based classification setup, LLM ensembles make up many of the top-scoring systems, while in the embedding setup, systems with pre- and post-processing on pretrained embedding models perform about on par with custom fine-tuned solutions. Our analysis identifies potential headroom for improvement of automated systems in both tracks. The task website includes visualizations of embeddings alongside instance-level classification results for all teams.