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Utilising high-level features in summarisation of academic presentations

Curtis, Keith, Jones, Gareth J.F. orcid logoORCID: 0000-0003-2923-8365 and Campbell, Nick (2017) Utilising high-level features in summarisation of academic presentations. In: ICMR’17 International Conference on Multimedia Retrieval, 6–9 June 2017, Bucharest, Romania. ISBN 978-1-4503-4701-3

Abstract
We present a novel method for the generation of automatic video summaries of academic presentations. We base our investigation on a corpus of multimodal academic conference presentations combining transcripts with paralinguistic multimodal features. We First generate summaries based on keywords by using transcripts created using automatic speech recognition (ASR). Start and end times for each spoken phrase are identiFied from the ASR transcript, then a value for each phrase created. Spoken phrases are then augmented by incorporating scores for human annotation of paralinguistic features. These features measure audience engagement, comprehension and speaker emphasis. We evaluate the effectiveness of summaries generated for individual presentations, created using speech transcripts and paralinguistic multimodal features, by performing eye-tracking evaluation of participants as they watch summaries and full presentations, and by questionnaire of participants upon completion of eye-tracking studies. Summaries were also evaluated for effectiveness by performing comparisons with an enhanced digital video browser
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Evalueation; Video summarisation; classification; evaluation; eye-tracking
Subjects:UNSPECIFIED
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: ICMR '17- Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval. . Association for Computing Machinery (ACM). ISBN 978-1-4503-4701-3
Publisher:Association for Computing Machinery (ACM)
Official URL:http://dx.doi.org/10.1145/3078971.3079028
Copyright Information:© 2017 ACM
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland through the CNGL Programme (Grant 12/CE/I2267) in the ADAPT Centre (www.adaptcentre.ie) at Dublin City University.
ID Code:23406
Deposited On:05 Jun 2019 13:52 by Thomas Murtagh . Last Modified 05 Jun 2019 13:52
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