\section{Conclusion}
\label{sec:conclusion}

We presented a contrastive learning framework for narrative similarity in SemEval 2026 Task 4, using sentence-transformer embeddings to capture theme, plot progression, and outcomes. Our Track A pipeline relies on single-view full-text embeddings with smart layer freezing, while Track B extends this with multi-view representations and alignment across narrative facets. Across both pseudonymized and raw settings, the approach delivers competitive performance while avoiding expensive generative inference, making it practical for large-scale narrative retrieval and representation learning.

\subsection*{Limitations}
Our models depend on synthetic training data and may not fully generalize to diverse narrative styles or domains without additional adaptation. The multi-view pipeline also relies on automatically extracted narrative elements, which can introduce noise into the learned representations. Due to the shared-task timeline, we were not able to conduct a dedicated manual quality audit of synthetic extractions (e.g., human verification of theme, plot-event coherence, and outcome fidelity), so residual extraction errors may remain unquantified. Finally, fixed fusion weights and temperature settings were tuned on the development data and may not be optimal for unseen domains.

\subsection*{Acknowledgement}
We thank the SemEval-2026 Task 4 organizers \cite{semeval2026task4} for creating the dataset and evaluation framework.
