1. A video summarization method comprising: providing a video wherein the video has a plurality of sentences and a plurality of frames; applying a key frame extraction step to the frames of the video to acquire a plurality of key frames, wherein the key frame extraction step comprises: computing the similarity between each frame to obtain a plurality of similarity values; and choosing the key frames from the frames, wherein the sum of the similarity values between the key frames is the minimum; applying a key sentence extraction step to the sentences of the video to acquire a plurality of key sentences, wherein the key sentence extraction step comprises: converting the sentences into a plurality of corresponding sentence vectors; computing the distance between each sentence vector to obtain a plurality of distance values; according to the distance values, dividing the sentences into a plurality of clusters, wherein the clusters are members of a set; computing the importance of each sentence of each cluster to obtain the importance of each cluster; applying a splitting step to split a most important member with the highest importance in the cluster into a plurality of new clusters, wherein the new clusters replace the original most important member and join the set as members of the set; repeating the splitting step until the number of the clusters reaches a predetermined value; and choosing at least one key sentence from each members of the set, wherein the sum of the importance of the key sentences is the maximum; and outputting the key frames and the key sentences.
4. The video summarization method according to claim 1 , wherein the step of computing the similarity between each frame uses an algorithm and the algorithm is selected from the group consisting of a color histogram similarity algorithm and a mutual information similarity algorithm.