Authors: Maitreya Prafulla Chitale, Uday Bindal, Rajakrishnan Rajkumar, Rahul Mishra
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.
Source: http://arxiv.org/abs/2410.14666v1
