\section{Related Work}

\subsection{Narrative Representation and Story Embeddings}
While traditional Semantic Textual Similarity (STS) focuses on lexical and sentence-level overlap, narrative similarity requires modeling higher-order structures. This follows the shift in the field towards ``Story Embeddings,'' as formalized by Hatzel et al. \cite{hatzel2024story}, who argue that determining story similarity requires moving beyond keyword matching to capture the underlying narrative graph.
Previous approaches have attempted to model stories hierarchically \cite{lee2020hierarchical}, often focusing on the sequential progression of events. More specific mechanisms, such as re-contextualization through attention \cite{biemann2021narrative}, have been proposed to better encode the flow of a narrative.

\subsection{Sentence Transformers and Backbone Architectures}
To operationalize narrative similarity efficiently, we build upon the Sentence-BERT framework \cite{reimers-gurevych-2019-sentence}, which enables fast and scalable dense text comparison. While foundational contrastive learning approaches \cite{gao2021simcse} excel at general semantic matching, off-the-shelf models are insufficient for fictional texts. Framing narrative comparison as a dense retrieval task requires models to encode deep structural elements rather than mere surface-level semantics \cite{hatzel2024story, younus-qureshi-2025-nlptuducd}. To address this gap and align the representation space with narrative theory, we systematically fine-tune MPNet backbone on the task-provided synthetic narrative triples.

\subsection{Multi-View and Data-Efficient Contrastive Learning}
The separation of narrative similarity into theme, plot, and outcome naturally frames our task as a multi-view learning problem. As demonstrated by Zhang et al. \cite{zhang-etal-2022-multi}, leveraging multiple perspectives of a document significantly improves dense representation compared to standard single-vector compression. We implement this through a shared transformer backbone coupled with aspect-specific projection heads, effectively decoupling complex narrative signals into orthogonal embedding spaces.
Furthermore, the limited availability of human-annotated narrative triples necessitates data-efficient training strategies. To overcome this scarcity and maximize model generalization, we incorporate synthetic data augmentation \cite{jiang-etal-2022-promptbert}.