Abstract
Nowadays, there are many researches trying to make computers tell stories. But there is no complete and structural definition for stories. The narrative theorists suggested that a story should be divided into three layers: fabula, lot, and presentation. And Swartjes offered an initial framework for the fabula layer. However, this framework only define six basic elements and four causal relations and still lack a lot of information that is necessary for storytelling, like the temporal relation between two elements. In this thesis, we define the temporal relation between two elements on original fabula model and construct a set of rules, which are used to determine under what condition we should join the temporal relation between two elements. Finally, we use an algorithm which is similar to Depth-first search (DFS) to transform fabula instance based on original model and instance with temporal relation into human-readable articles. And we let general viewers judge which article is more understandable. The results are that the articles from fabula instance with temporal relations get higher scores, and the opinions to these articles are more centralized.