Auto-summarization of multimedia meeting records based on accessing log

Abstract

Computer techniques have been leveraged to record human experiences in many public spaces, e.g. meeting rooms and classrooms. For the large amount of such records produced after long-term use, it is imperative to generate auto summaries of the original content for fast skimming and browsing. In this paper, we present ASBUL, a novel algorithm to produce summaries of multimedia meeting records based on the information about viewers' accessing patterns. This algorithm predicts the interestingness of record segments to the viewers based on the analysis of previous accessing patterns, and produces summaries by picking the segments of the highest predicted interests. We report a user study which compares ASBUL-generated summaries with human-generated summaries and shows that ASBUL algorithm is generally effective in generating personalized summaries to satisfy different viewers without requiring any priori, especially in free-style meetings where information is less structured and viewers' understandings are more diversified. © Springer-Verlag Berlin Heidelberg 2005.