Movie summarization using bullet screen comments

Automatic movie summarization helps users to skim a movie in an efficient way. However, it is challenging because it requires the computer to automatically understand the movie content and the users’ opinions. Most previous works rely on the movie data itself without considering the opinions of the audience. In this paper, a novel approach for automatic movie summarization is presented by exploring a new type of user-generated data, i.e. bullet screen comments, which allow the audience to comment on the movie in a real-time manner. The number of the comments on a movie segment shows the exciting degree of the audience, while the content of the comments includes the concepts (e.g. the characters and the scenes) that interest the audience. In our approach, given a movie, bullet screen comments are utilized to select candidate highlight segments which are the most commented. Then the candidates are scored based on the number and the content of the bullet screen comments. Visual diversity is also considered in the scoring process. Finally, a subset of candidates which achieves the highest score is selected to compose a summary. Our experiments carried out on movies of different genres have shown the effectiveness of our proposed approach.