DiscoGraMS：利用电影角色感知话语图增强电影剧本摘要生成
DiscoGraMS: Enhancing Movie Screen-Play Summarization using Movie Character-Aware Discourse Graph
AI总结：
针对电影剧本摘要中模型难以捕获长程依赖和潜在关系的问题，本文提出DiscoGraMS资源，将剧本表示为角色感知话语图，结合后期融合基线方法，初步取得有前景结果。
摘要：
与标准文档摘要相比，电影剧本摘要面临一系列独特的挑战。剧本不仅篇幅冗长，而且具有角色、对话和场景之间复杂的相互作用，包含众多直接和微妙的关系及上下文细微差别，这些难以被机器学习模型准确捕获和理解。近期剧本摘要的尝试侧重于微调基于Transformer的预训练模型，但这些模型在捕获长期依赖和潜在关系方面往往表现不足，并且经常遇到“中间丢失”问题。为应对这些挑战，我们引入了DiscoGraMS，一种新颖的资源，将电影剧本表示为电影角色感知话语图（CaD图）。该方法非常适合各种下游任务，如摘要生成、问答和显著性检测。该模型旨在保留所有显著信息，提供对剧本内容更全面、更忠实的表示。我们进一步探索了一种基线方法，通过图模态和文本模态的后期融合将CaD图与相应电影脚本相结合，并展示了非常初步的有前景结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
