\begin{abstract}
    We present our approach to \textit{SemEval 2026 Task 4: Narrative Story Similarity and Narrative Representation Learning}. Our solution uses contrastive learning with fine-tuned sentence transformers to capture narrative similarity across abstract themes, course of action, and outcomes. We develop two pipelines: (Track A) a single-view method that encodes full narratives with smart layer freezing to reduce overfitting, and (Track B) a multi-view method that models theme, plot, and outcome with view-specific projection heads and self-supervised alignment. Both pipelines build on sentence-transformers models and are trained with contrastive loss on synthetic data. The code is available at the following GitHub repository: \href{https://github.com/dinhthienan33/SemEval2026-Task4-ttda704}{https://github.com/dinhthienan33/SemEval2026-Task4-ttda704}.
    \end{abstract}