Automatic text processing
Passive capture and structuring of lectures
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Auto-summarization of audio-video presentations
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Learning video browsing behavior and its application in the generation of video previews
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
A user attention model for video summarization
Proceedings of the tenth ACM international conference on Multimedia
Integrating Meeting Capture within a Collaborative Team Environment
UbiComp '01 Proceedings of the 3rd international conference on Ubiquitous Computing
Video summarization based on user log enhanced link analysis
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
ETP '03 Proceedings of the 2003 ACM SIGMM workshop on Experiential telepresence
Ontology and Taxonomy Collaborated Framework for Meeting Classification
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Architecture and Components for Capture and Access Applications
LA-WEBMEDIA '04 Proceedings of the WebMedia & LA-Web 2004 Joint Conference 10th Brazilian Symposium on Multimedia and the Web 2nd Latin American Web Congress
INFERS: an infrastructure for experience record in smart spaces
ICWL'06 Proceedings of the 5th international conference on Advances in Web Based Learning
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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.