## 1 Introduction  
Computational narrative analysis is essential to provide explainable services that deal with narrative multimedia (i.e., creative works that contain stories and are distributed through multimedia). Although stories are key features that influence user affection, the existing applications (e.g., Netflix and Youtube) provide their services only based on metadata, user history, or manual annotations [1]. Therefore, various studies have attempted to analyze and understand stories computationally. However, most of these studies have remained in statistical analysis rather than meanings of stories and components of the stories. This self-restriction makes them unable to reach analyzing plot structures (i.e., how events in stories are logically connected). Most of the existing studies analyzed stories based on social networks between characters (called as character networks) that appeared in narrative multimedia [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]. Thisapproach is based on an intuition that stories are developed by interactions between characters [14]. Character networks exhibited a noticeable performance in extracting fragmentary information from stories, e.g., importance of characters [2; 4; 15], communities among characters [2; 3; 6], major events [6; 16], and so on. Nevertheless, features that character networks reflect are far not only from stories (i.e., a series of events that happen in the narrative world) but also from plots (i.e., a series of events that are logically connected). Since they have not considered context and story lines, this problem is inevitable. Moreover, stories consist of multiple story lines, and the story lines are complicatedly entangled, as described in Figure 1. This point creates difficulties in computationally analyzing stories' context. To analyze stories' context, a few studies [10; 11; 18] have attempted to reveal plot structures of narrative works. They concentrated on discriminating story lines in a story. Although story lines in a story describe events in the same narrative world, they also have independent narrative arcs, as illustrated in Figure 1. Therefore, classifying events into story lines, which are sets of more logically connected events, is a starting point for analyzing plot structures and understanding stories' context. Some story lines present events around protagonists, and others deal with side branches of a story. If a story line is more focused on events related to protagonists and main conflicts, we call it'main plot.' The other story lines are called'subplots.' The 'plot structure' indicates the way in which a story is interwoven with multiple subplots. Each main plot and subplot is split into multiple fragments, and authors/directors present the fragments alternatively. A fundamental principle for composing a plot structure is that events in a story must be located to escalate conflicts around its protagonist. Therefore, models for typical plot structures (e.g., [PERSON]'s pyramid [19] and arch plot [17]) commonly say that conflicts in a story should be intensified until the climax and then resolved. In most of the stories, a climax is placed much closer to a denouement than an exposition to maintain users' interests until the end [17]. Nevertheless, if conflicts are gradually escalated until the climax, users will be exhausted. Thus, in practice, authors/directors control conflicts in stories by interweaving multiple story lines, as displayed in Figure 1.  
The existing studies [10; 11; 18] attempted to distinguish main plots and subplots based on the changes in the social relationships of characters. This approach is based on a notion  
Figure 1: An example of plot structure decomposition. The left chart describes a plot structure of a story that is interwoven with one main plot (M) and two subplots (S1 and S2).