## 2 Preliminaries and Literature Review
### Plot Analysis Based on Character Networks  
This section introduces a few studies that have attempted to analyze plots in narrative multimedia based on the character network. Although various studies claimed their contributions to be analyzing or extracting plots, they used the term 'plots' to mean context or flow of stories. Therefore, most of the existing studies have not considered that a plot is interwoven with plural main plots and subplots. However, without understanding how main plots and subplots are entangled, we cannot distinguish which scenes are main plots. In addition, to find the meanings and functions of each scene, revealing plot structures is necessary. [PERSON] [42; 43] attempted to compare plot structures in novels. The author has proposed a method for measuring the similarity of plot structures based on the similarity of characters. The character similarity was estimated by using three features: (i) Social relationships between characters, (ii) emotional words, and (iii) descriptive words that are used to describe characters. The author has also proposed a kernel function for measuring character similarity based on time-sequential changes in the three features. To extend character similarity to plot structure similarity, the author calculated similarity between all characters that appeared in two novels. The plot structure similarity between the two novels was estimated by a summation of all the similarities between characters from each novel. In the studies of [PERSON] [42; 43], the plot structure indicates dynamic changes in characters regarding both (i) changes in each character and (ii) compositions of the characters. Finally, the author attempted to represent plot structures by using time-sequential fluctuations of the three features. Changes in characters might partially reflect connectivity between events since the events cause and trigger the changes. Nevertheless, this approach oversimplifies the plot structure. Since a plot structure is interwoven with multiple story lines, we have to consider that characters will show different directions (or amount) of changes in each story line. The methods of [PERSON] [42; 43] will be applicable after decomposing plot structures into individual story lines. [PERSON] [44] proposed a method for simulating plot structures. This method uses a similar approach to the study of [PERSON] [43] but is much simpler. The author attempted to expose transitions between story lines by using changes in the occurrence frequency of names of each character. This approach assumes that the frequency of names reflects how much a story is focused on each character (called 'narrative attention'). [PERSON] [44] also proposed a method for simulating the narrative attention and interactions between characters. However, the purpose of this simulation is not to decompose plot structures into story lines but to combine story lines into an optimal plot structure. In another study, [PERSON] [45] also proposed a method for simulating changes in character networks to generate stories from character networks. [PERSON] et al. [46] have discovered typical plots of novels by clustering the novels according to time-sequential changes in sentiments. Similar to the study of [PERSON] [47], they measured the sentiments of words in novels and modeled changes in the sentiments time-sequentially. They named this model 'emotional arc.' Although they found six typical plots by clustering the emotional arcs, this approach is too superficial to analyze plots semantically. Since these three approaches [43; 44; 46] focused on textual narrative works (e.g., novels), it is difficult to expand them to various kinds of narrative multimedia. However, they commonly exhibited a necessity of a time-sequential analysis of plots. A few studies have proposed methods for decomposing plot structures into story lines. However, as we discussed, most of these studies only relied on occurrences of characters or relationships between characters. [PERSON] et al.