\section{Introduction}
\label{sec:intro}

Understanding and comparing complex narratives is a fundamental yet persistent challenge in Natural Language Processing. The significance of this problem is highlighted by consecutive SemEval competitions dedicated to narrative analysis ranging from measuring the multidimensional similarity of multilingual news stories \cite{chen-etal-2022-semeval} to characterizing and extracting framing narratives from online media \cite{piskorski-etal-2025-semeval}. Fictional story similarity demands robust representation learning techniques that extend far beyond simple lexical overlap or factual event extraction. To effectively map the architecture of a story, computational models must isolate and encode deep latent dimensions, such as abstract themes, the progression of the plot, and final resolutions \cite{hatzel2024story}.

Historically, handling severe narrative ambiguity has proven extremely difficult. Even advanced generative models frequently falter when faced with scenarios possessing multiple valid interpretations, highlighting the necessity for rigid structural scaffolding to guide model reasoning \cite{keluskar2024can}. Furthermore, attempting to resolve these uncertainties at inference time using computationally heavy mechanisms such as generating multiple reasoning paths via self-consistency voting \cite{wang2023self} introduces massive latency. This makes LLM-based approaches highly impractical for scalable story retrieval, embedding extraction, or large-scale comparative tasks. 

To overcome these limitations, our work shifts away from relying on expensive generative inference at prediction time and instead focuses on efficient narrative representation learning. Inspired by cognitive dual-process theories, which suggest that human cognition adapts its processing depth based on the complexity of the information \cite{kahneman2011thinking}, we use LLMs only in a limited offline role for synthetic data generation and narrative structure extraction, while keeping the final similarity system itself embedding-based and efficient at inference time.

The main contributions of our work are:
\begin{itemize}
    \item{LLM-augmented Narrative Representation.} A deterministic framework that leverages LLMs for high-quality data augmentation and structural extraction, while utilizing fine-tuned sentence transformers for final similarity inference.
    \item{Multi-view structural modeling.} A contrastive learning approach that decomposes stories into theme, course of action, and outcome via view-specific projection heads with self-supervised alignment.
    \item{Robust contrastive training.} Triplet-margin optimization with smart layer freezing to improve generalization while preserving narrative nuance.
\end{itemize}