Hierarchical Topic Segmentation of TV shows Automatic Transcripts

Overview & Synopsis

The growth in the collections of multimedia documents made the development of new data access and data structuring techniques a necessity. The work presented in this report focuses on structuring TV shows and among the different kinds of structuring we approach the topic segmentation. Moreover we are interested in techniques able to provide hierarchical topic segmentation. The motivation for this research is defined by the potential impact of these techniques, since they fold perfectly on navigation and information retrieval subjects. In order to provide an automatic structuring of TV shows, that is generic, we use the words pronounced in TV shows, made available by their automatic textual transcription provided by an ASR system. The proposed topic segmentation algorithm consists in the recursive application of a modified version of TextTiling. It is based on the exploitation of a technique called vectorization, which was recently introduced for linear segmentation and outperformed the other existing techniques. We decided to study vectorization in more depth since it is a powerful technique and we tested it both for linear and hierarchical segmentation. The results obtained show that using vectorization can improve the segmentation and justify the interest of further applying such a technique.