Learning on demand - course lecture distillation by information extraction and semantic structuring for spoken documents

This paper presents a new approach of organizing the course lectures (as spoken documents) for efficient learning on demand by the users. By the properly matching the course lectures with the slides used, we divide the course lectures into hierarchical ldquomajor segmentsrdquo with variable length based on the topics discussed. Key term extraction, hierarchical summarization and semantic structuring are then performed over these ldquomajor segmentsrdquo. A key term graph is also constructed, based on which the various major segments of the course can be linked. In this way, the user can ask questions to the system, and develop his own road map of learning the knowledge he needs considering his available time and his background knowledge, based on the semantic structure provided by the system. A preliminary prototype system has been successfully developed with encouraging initial test results.