NCL&HKU-NarrSim at SemEval-2026 Task 4: Aspect-Based Agents and Supervised Contrastive Embeddings for Narrative Similarity

AbstractSemEval-2026 Task 4 on Narrative Similarity requires models to assess narrative alignment between stories rather than relying on surface lexical similarity. For Track A, we introduce the Aspect-Based Narrative Similarity Agents(ABNS-Agents), a two-stage agent-based framework. It extracts three core narrative aspects aligned with the task definition under a schema constraint, and then performs aspect-aligned similarity adjudication using an LLM decision model. For Track B, Narrative Supervised Contrastive Embeddings(NSConE) is based upon supervised contrastive learning to model narrative similarity. Our experiments show that ABNS-Agents achieves 70.25% accuracy on the test set, while NSConE reaches 68.5% test accuracy, demonstrating competitive performance across both reasoning-based and representation-learning paradigms. The findings highlight the effectiveness of aspect-aligned structured modelling and task-specific supervised contrastive learning for capturing narrative similarity beyond surface semantics.