Paper Info
Title
A Keyword-based Video Summarization Learning Platform with Multimodal Surrogates
Abstract
In general, video-based learning contains rich media information, but displaying an entire video linearly is time-consuming. As an alternative, video summarization techniques extract important content provides short but informative fragments. In this paper, a video learning platform (KVSUM: Keyword-based Video Summarization) is presented that integrates image processing, text summarization, and keyword extraction techniques. Without human annotators, the learning platform can process an input video and transform it into online learning materials automatically. The video frames are first split from a given video while the transcription is used to generate the text summary and keywords. In the current study, the video surrogates are composed of extracted keywords, text and video summaries, and video frames. In other words, KVSUM is able to provide both visual and verbal surrogates. In order to validate the effect of surrogates in the KVSUM, a comparison with another video surrogate, the fast forward (FF), to evaluate learners' comprehension to video contents. Sixty undergraduate students took part in examining two different video surrogates. The experimental results show that KVSUM had a more positive effect than FF in comprehension to videos. In terms of system usage and satisfaction, KVSUM is significantly more attractive than FF to learners.
Year
DOI
Venue
2011
10.1109/ICALT.2011.19
ICALT
Keywords
Field
DocType
video surrogate,multimedia systems,video summarization technique,multimedia learning,image processing,online learning materials,keyword-based video summarization learning,keyword-based video summarization learning platform,video summarization,video summary,video content,video-based learning,keyword extraction techniques,keyword cloud,different video surrogate,video coding,input video,media information,multimodal surrogates,entire video linearly,text analysis,video communication,kvsum,computer aided instruction,text summarization,video frame,visualization,materials,reliability,databases
Automatic summarization,Virtual learning environment,Text mining,Information retrieval,Computer science,Keyword extraction,Visualization,Image processing,Video quality,Multimedia,Comprehension
Conference
ISSN
ISBN
Citations 
2161-3761 E-ISBN : 978-0-7695-4346-8
978-0-7695-4346-8
2
PageRank 
References 
Authors
0.36
11
3
Name
Order
Citations
PageRank
Wen-Hsuan Chang1101.74
Jie-Chi Yang235043.91
Yu-Chieh Wu324723.16