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Video smmarization is a crucial method to reduce the time of videos whichreduces the spent time to watch/review a long video. This apporach has becamemore important as the amount of publisehed video is increasing everyday. Asingle or multiple videos can be summarized into a relatively short video usingvarious of techniques from multimodal audio-visual techniques, to naturallanguage processing approaches. Audiovisual techniques may be used to recognizesignificant visual events and pick the most important parts, while NLPtechniques can be used to evaluate the audio transcript and extract the mainsentences (timestamps) and corresponding video frames from the original video.Another approach is to use the best of both domain. Meaning that we can useaudio-visual cues as well as video transcript to extract and summarize thevideo. In this paper, we combine a variety of NLP techniques (extractive andcontect-based summarizers) with video processing techniques to convert a longvideo into a single relatively short video. We design this toll in a way thatuser can specify the relative length of the summarized video. We have alsoexplored ways of summarizing and concatenating multiple videos into a singleshort video which will help having most important concepts from the samesubject in a single short video. Out approach shows that video summarizing is adifficult but significant work, with substantial potential for further researchand development, and it is possible thanks to the development of NLP models.

Beyond the Frame: Single and mutilple video summarization method with user-defined length