  Video smmarization is a crucial method to reduce the time of videos which
reduces the spent time to watch/review a long video. This apporach has became
more important as the amount of publisehed video is increasing everyday. A
single or multiple videos can be summarized into a relatively short video using
various of techniques from multimodal audio-visual techniques, to natural
language processing approaches. Audiovisual techniques may be used to recognize
significant visual events and pick the most important parts, while NLP
techniques can be used to evaluate the audio transcript and extract the main
sentences (timestamps) and corresponding video frames from the original video.
Another approach is to use the best of both domain. Meaning that we can use
audio-visual cues as well as video transcript to extract and summarize the
video. In this paper, we combine a variety of NLP techniques (extractive and
contect-based summarizers) with video processing techniques to convert a long
video into a single relatively short video. We design this toll in a way that
user can specify the relative length of the summarized video. We have also
explored ways of summarizing and concatenating multiple videos into a single
short video which will help having most important concepts from the same
subject in a single short video. Out approach shows that video summarizing is a
difficult but significant work, with substantial potential for further research
and development, and it is possible thanks to the development of NLP models.
