Automated generation of news content hierarchy by integrating audio, video, and text information

This paper addresses the problem of generating semantically meaningful content by integrating information from different media. The goal is to automatically construct a compact yet meaningful abstraction of the multimedia data that can serve as an effective index table, allowing users to browse through large amounts of data in a non-linear fashion with flexibility, efficiency, and confidence. We propose an integrated solution in the context of broadcast news that simultaneously utilizes cues from video, audio, and test to achieve the goal. Some experimental results are presented and discussed.