An automatic storytelling system based on natural language processing

With the rapid development of science and technology, high technology tools have been widely used in educational field. Recent studies reported thatintelligent social robots are being widely used in early childhood education. Artificialintelligence technology is playing a crucial role in the application of robots. Storytelling is one of the functions where robots serve as learning companions. Automatic storytelling is a challenging text generation task since it requires generating long,coherent natural language to describe a sensible sequence of events.This project aims to build an automatic system that can tell a story based on givensentences, and the performance of the system needs to be evaluated and the output should be evaluated how well they can be perceived by humans. The system consists of two main components: keyword extraction module and story generation module.The keyword extraction module is designed to extract keywords from the input storyand generate a corpus. The thesis choose to use topic model to extract the keywordsof each story. The storytelling generator module should be able to automatically generate pieces of the story with a coherent natural language based on the sequence of given events. We build the generation module based on the pre-trained model,GPT-2. According to evaluation results, the system performs well and have shown that the stories generated from our system can be well perceived by humans. Keywords: natural language processing, storytelling, keyword extraction, text generatio