D isco G ra MS : Enhancing Movie Screen-Play Summarization using Movie Character-Aware Discourse Graph (2025)

Abstract: 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.

## introduction text summarization has been extensively studied within the nlp community @xcite @xcite . recently, large language models (llms) have demonstrated human-level performance in this area @xcite . however, summarizing long documents remains a challenge for even the most advanced llms, as their effectiveness can be influenced by the location of 1 https://github.com/maitreya152/discograms 2 https://huggingface.co/datasets/maitreya152/cad_graphs salient information within the text @xcite . for language models to effectively utilize information within very long input documents, their performance should exhibit minimal sensitivity to the positional placement of relevant information within the input @xcite . movie script or screenplay summarization @xcite is a relatively hard task compared to standard document summarization due multitude of reasons. movie scripts are typically very long documents characterized by intricate narratives, numerous subplots, and substantial dialogue, which pose significant challenges for summarizing the content without losing the core elements of the story. many of the movie scripts have non-linear flow of events such as flashbacks, flash-forwards, and parallel plot lines, making the summary to retain the coherence and original flow. to address this, we present discograms, an innovative resource that represents movie scripts as a character-aware discourse graph (cad graph). this graph captures the core essence of the movie plot by modeling latent relationships among key elements, including characters, the scenes they participate in, and the dialogues they deliver, thereby highlighting all possible semantically important aspects of the narrative. the cad graph captures intricate nuances and the interplay between characters and scene sequences, effectively addressing challenges like flashbacks and sudden plot twists that are difficult to capture using only textual content. the main contributions of this work are as follows: 1) we introduce, for the first time to our knowledge, a movie character-aware discourse graph (cad graph) specifically designed for movie script summarization. 2) we propose a late modality fusion model that combines both cad graphs and textual content for improved movie script summarization. 3) we perform an ablation study to demonstrate the effectiveness of cad graphs in enhancing summarization. ## related work since the origin of modern graph theory in 1736 with euler's proof the seven bridges of königsberg problem i.e. traversing a city crossing 7 bridges exactly once @xcite , graph representations have been used to model data in diverse fields like chemistry, biology and computer science. linguistic data has also been represented as graph structures like dependency representations @xcite and successfully deployed in nlp applications. the idea of representing entire texts as graphs was proposed in seminal work by @xcite . they created graphs comprising of nodes which keywords connected to other words located within a window of 2 to 10 words. this approach was extremely effective for the task of extractive summarization. more recently, @xcite show the efficacy of this technique for abstractive summarization of scientific articles. here, entities in the text served as nodes (with coreferential entity clusters represented as a single node) connected to one another via labelled edges depicting relationships (like hyponymy) between nodes. @xcite proposed a movie recommendation system using character graph embeddings to model relationships for movie similarity while @xcite propose a model for summarizing movie videos by constructing a sparse graph using only the turning point scenes from videos. in contrast, our cad graph method integrates scene, dialogue, and character interactions and focuses on summarizing movie text scripts, which presents a distinct set of challenges due to the long-form nature of screenplay texts. prior work has explored character-based graphs in narratives. @xcite introduced sinnet, a system for extracting social interaction networks from text. @xcite focused on inferring interpersonal relationships in narrative summaries, while @xcite developed methods for extracting social networks from literary fiction. @xcite propose dualenc to bridge the structural gap in data-to-text generation by integrating graph and sequential representations. our work builds on these approaches by constructing a cad graph to enhance screenplay summarization. there have been no significant efforts to employ graphs for movie script summarization. only recently, @xcite adapted textrank @xcite , a sentence centrality-based graph approach, for movie scripts. however, this approach was outperformed by the simpler longformer encoder-decoder (led) model @xcite ) by large margin. ## dataset we use the moviesum @xcite dataset, a comprehensive resource for movie summarization, containing 2,200 movie screenplays along with metadata and plot summaries, including movies up to 2023. the plot summaries are sourced from imdb and wikipedia, ensuring a diverse range of writing styles and perspectives. the summaries were generated through a combination of automatic extraction and manual curation by trained annotators. the scripts are in xml format, preserving key elements such as scene descriptions, dialogues, and character names for efficient analysis. the dataset is split into training (1,800 movies), validation (200 movies), and test (200 movies) sets, with average screenplay lengths of 29k words and summaries of 717 words. the summaries, sourced from imdb and wikipedia, blend automatic extraction and manual curation. analysis reveals a high level of abstractiveness in the summaries, indicated by novel 3-grams and 4-grams not found in the original scripts. ## methodology in this section, we describe the process of constructing the character-aware discourse graph (cad graph) from movie scripts. we then present a baseline method that leverages both the cad graph and the textual content of the scripts, using a late modality fusion approach to generate movie script summaries. ## results we select the models longt5 @xcite , pegasus-x @xcite , and the longformer encoder-decoder (led) model @xcite , (see ## discussion our experiments on abstractive summarization of movie screenplays (i.e., the process of generating a plot summary given a screenplay) show that representing screenplays as graphs consisting of scenes, dialogues, and characters holds a lot of promise for movie summarization. to show how our characteraware graphs capture the roles of different characters in the graph and represent them, we plot the extracted node embeddings from our model in figure 3 . first, the node embeddings of all the nodes in the graph are extracted by passing the required movie graph over the gnn part of the final trained model. next, the acquired node embeddings are filtered so that they only contain the node embeddings of the movie characters, and other node embeddings such as those of scenes and dialogues are discarded. once the character node embeddings are extracted, they are analyzed using principal component analysis (pca) to reduce their dimensionality while preserving essential variance. we employ pca to project the high-dimensional embeddings into a three-dimensional space, allowing for better visualization and interpretability. the transformed embeddings are then clustered using the k-means algorithm, which groups characters into distinct clusters based on their learned representations. to further illustrate the relationships and roles of different characters, we visualize the clusters in a three-dimensional scatter plot, where each point represents a character, and the color corresponds to the assigned cluster through k-means clustering. this visualization enables us to observe meaningful patterns in the character representations. characters who frequently interact or share similar narrative functions often appear closer together, whereas those with distinct roles are more clearly separated. the clustering also helps to reveal latent groupings, such as protagonists, antagonists, and supporting characters, as also depicted in figure 3 . this demonstrates how our approach successfully captures narrative structures through graph-based representation learning. the effectiveness of our method in clustering and analyzing character embeddings suggests that our gnn-based approach learns informative representations that reflect underlying narrative and character dynamics. ## conclusion our approach outperforms quantitative results (except r-l) reported in prior work on movie summarization using the same dataset @xcite . we attribute the better performance of our system to the presence of richer graphs, and encoding schemes. specifically, we attribute the phenomenal improvement in bert score to the introduction of an attention layer to combine the encodings of the chunks as discussed in section a.1 and the novel cad graph which enables the model to easily retain salient information which is validated by the high bert scores. we suspect that the low scores obtained in r-l are mainly due to the lower context size model (led 4k) due to a restriction on the available compute resources. the model's (led) low performance in isolation validates our believes. our results indicate that knowledge-based representations of the text and plot structure help deep learning algorithms. we expect our approach to have implications for other nlp problems like question-answering, genre identification, and saliency detection. @xcite propose a system to represent narrative text consisting of passages as nodes connected by edges encoding cognitive relations between them. in addition to mainstream engineering applications, our graph representations can be deployed in scientific studies of inferencing processes in narrative comprehension by humans.