\section{Task Description}

\textbf{SemEval-2026 Task 4: Narrative Story Similarity and Narrative Representation Learning} challenges participants to develop systems capable of identifying similarities between stories based on their underlying narrative structures rather than surface-level lexical overlaps \cite{semeval2026task4}.

\subsection{Definition of Narrative Similarity}
The task organizers define narrative similarity through \textbf{a specific schema consisting of three core aspects}:

\begin{enumerate}
    \item \textbf{Abstract Theme:} This encompasses the defining constellation of problems, central ideas, and core motifs (e.g., a struggle against nature, a coming-of-age journey).
    \item \textbf{Course of Action:} This describes the sequence of events, conflicts, and turning points. It focuses on the chronological order and the causal links between actions (e.g., a protagonist ignores a warning, suffers a loss, and then recovers).
    \item \textbf{Outcomes:} This refers to the resolution of the plot, such as the fate of the characters or the moral lesson learned. The guidelines emphasize that similar themes can lead to polar opposite outcomes, which must be distinguished.
\end{enumerate}

\subsection{Subtasks}

\begin{itemize}
    \item \textbf{Track A (Triple Classification):} The system is presented with an \textit{Anchor} story and two candidates, \textit{Story A} and \textit{Story B}. The objective is to perform a binary classification to determine which candidate is narratively closer to the Anchor based on the definitions above.
    \item \textbf{Track B (Representation Learning):} The system must generate vector embeddings for individual stories such that the cosine similarity between the vectors reflects their narrative affinity.
\end{itemize}
