Serialized TV shows are built on complex storylines that can be hard to track and evolve in ways that defy straightforward analysis. This paper introduces a multi-agent system designed to extract and analyze these narrative arcs. Tested on the first season of Grey's Anatomy (ABC 2005-), the system identifies three types of arcs: Anthology (self-contained), Soap (relationship-focused), and Genre-Specific (strictly related to the series' genre). Episodic progressions of these arcs are stored in both relational and semantic (vectorial) databases, enabling structured analysis and comparison. To bridge the gap between automation and critical interpretation, the system is paired with a graphical interface that allows for human refinement using tools to enhance and visualize the data. The system performed strongly in identifying Anthology Arcs and character entities, but its reliance on textual paratexts (such as episode summaries) revealed limitations in recognizing overlapping arcs and subtler dynamics. This approach highlights the potential of combining computational and human expertise in narrative analysis. Beyond television, it offers promise for serialized written formats, where the narrative resides entirely in the text. Future work will explore the integration of multimodal inputs, such as dialogue and visuals, and expand testing across a wider range of genres to refine the system further.

翻译：系列电视剧建立在复杂的故事线上，这些故事线难以追踪，且其演变方式难以进行直接分析。本文介绍了一种旨在提取和分析这些叙事弧线的多智能体系统。该系统在《实习医生格蕾》（ABC 2005-）第一季上进行了测试，识别出三种类型的弧线：选集型（独立成章）、肥皂剧型（以关系为核心）和类型特定型（严格与剧集类型相关）。这些弧线的逐集进展被存储在关系型和语义（向量）数据库中，从而支持结构化的分析和比较。为了弥合自动化与批判性解读之间的差距，该系统配有一个图形界面，允许用户使用工具对数据进行人工细化和可视化。该系统在识别选集型弧线和角色实体方面表现强劲，但其对文本副文本（如剧集摘要）的依赖，在识别重叠弧线和更微妙的动态方面显示出局限性。该方法凸显了在叙事分析中结合计算与人类专业知识的潜力。除了电视领域，它也为完全基于文本的系列化书面形式提供了应用前景。未来的工作将探索整合多模态输入（如对话和视觉信息），并在更广泛的类型范围内进行测试，以进一步完善该系统。