Narrative similarity extends beyond standard semantic tasks, requiring alignment of temporal, causal, and emotional structures. We present StoryNet, a framework that represents stories as heterogeneous graphs with character, event, and theme nodes. Stories are decomposed into structured narrative facets using large language models, and similarity is evaluated through both weighted semantic facet comparison and a graph neural network trained with contrastive learning. We analyze how integrating symbolic structure with learned graph representations compares to purely embedding-based baselines.

Summarizing movie screenplays presents a unique set of challenges compared to standard document summarization. Screenplays are not only lengthy, but also feature a complex interplay of characters, dialogues, and scenes, with numerous direct and subtle relationships and contextual nuances that are difficult for machine learning models to accurately capture and comprehend. Recent attempts at screenplay summarization focus on fine-tuning transformer-based pre-trained models, but these models often fall short in capturing long-term dependencies and latent relationships, and frequently encounter the “lost in the middle” issue. To address these challenges, we introduce DiscoGraMS, a novel resource that represents movie scripts as a movie character-aware discourse graph (CaD Graph). This approach is well-suited for various downstream tasks, such as summarization, question-answering, and salience detection. The model aims to preserve all salient information, offering a more comprehensive and faithful representation of the screenplay’s content. We further explore a baseline method that combines the CaD Graph with the corresponding movie script through a late fusion of graph and text modalities, and we present very initial promising results. We have made our code and dataset publicly available.