Topic-level Extractive Summarization of Lectures and Meetings Using a Snippet Similarity Graph

In 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 different scenarios on Video Lectures, to emphasize the merits of ease in utilizing summarizer with such content-based recommendation technique.