000199809 001__ 199809
000199809 005__ 20190316235927.0
000199809 037__ $$aREP_WORK
000199809 088__ $$aIdiap-RR-09-2014
000199809 245__ $$aTopic-Level Extractive Summarization of Lectures and Meetings Using a Snippet Similarity Graph
000199809 269__ $$a2014
000199809 260__ $$bIdiap$$c2014
000199809 336__ $$aReports
000199809 520__ $$aIn this paper, we present an approach for topic-level video snippet-based extractive summarization, which relies on con tent-based recommendation techniques. We identify topic-level snippets using transcripts of all videos in the dataset and indexed these snippets globally in a word vector space. Generate snippet cosine similarity scores matrix, which are then utilized to compute top snippets to be utilized for summarization. We also compare the snippet similarity globally across all video snippets and locally within a video snippets. This approach has performed well on the AMI meeting corpus, in terms of ROUGE scores compare to state-of-the-art methods. Experiments showed that corpus like AMI meeting has large overlap between global and local snippet similarity of 80% and the ROUGE scores are comparable. Moreover, we applied proposed TopS summarizer in dierent scenarios on Video Lectures, to emphasize the merits of ease in utilizing summarizer with such content-based recommendation technique.
000199809 700__ $$aBhatt, Chidansh A.
000199809 700__ $$aPopescu-Belis, Andrei
000199809 8564_ $$uhttps://infoscience.epfl.ch/record/199809/files/Bhatt_Idiap-RR-09-2014.pdf$$zn/a$$s1026448$$yn/a
000199809 909C0 $$xU10381$$0252189$$pLIDIAP
000199809 909CO $$qGLOBAL_SET$$pSTI$$ooai:infoscience.tind.io:199809$$preport
000199809 937__ $$aEPFL-REPORT-199809
000199809 970__ $$aBhatt_Idiap-RR-09-2014/LIDIAP
000199809 973__ $$aEPFL
000199809 980__ $$aREPORT