This study aims to clarify the composition of story content to help computers understand stories. In story content, the events peculiar to a genre occur intermittently. For example, school and sports festivals appear in school-themed genres. These events can trigger a story because they cause changes in the internal characteristics and relationships between characters, which in turn trigger the progress of the story. If computers can determine the events in a story, they will help understand its composition. Each story-event contains many strongly related words. For example, ``relay'' and ``runner'' appear in sports festival episodes. Therefore, investigating these tendencies is expected to contribute to the estimation of story-events. However, the amount of information obtained from comic texts is limited because they use illustrations and texts in a complementary manner. This makes it difficult for computers to obtain words from comics that characterize a story-event. To address this problem, we focused on the content similarities between comics and light novels. In this study, we estimated story-events in comics using the tendency of story-event words to appear in light novels. The results of this experiment indicate that we can calculate story-events in comics using a dictionary of story-events created by the proposed method.