FrameRank: A Text Processing Approach to Video Summarization
Table 2: We compare our approach with state-of-the-art methods on SumMe, TVSum and UGSum datasets.
['[BOLD] Dataset', '[BOLD] Method', '[BOLD] F-score']
The highlighted numbers indicate FrameRank obtains the best performance in the corresponding setting. We achieve the highest overall F-score of 0.453 on SumMe dataset and 0.601 on TVSum dataset (the previous state-of-the-art published was 0.429 and 0.596 Furthermore, we also carry on experiments on our UGSum52 dataset. Ours is superior to the other two methods where F-score is 0.388. It shows not only the effectiveness of the proposed FrameRank method, but also the reliability of the proposed temporal segmentation method (by comparing FrameRank and Uniform + FrameRank methods). It proves our method is able to find important segments to produce an informative summary. The results demonstrate the proposed method can create video summaries closer to human-level performance than other methods. Meanwhile, it is interesting to see our result is better than all the supervised methods. We analyze there are no standard rules to define what important content is to summarize video. Thus, human generated summaries may be quite different from each other due to different human perception and personal experience. We believe training data for user-generated video summarization is not sufficient for supervised methods, and the generated model is not able to characterize the property to summarize videos. Moreover, since TVSum is slightly different from SumMe and UGSum52, where it only contains 10 categories of videos, Thus, in theory, the characteristic of TVSum should be suitable for supervised methods to learn video structure, while our FrameRank still performs better. Therefore, we have reasons to believe a good unsupervised method is more appropriate for user-generated video summarization.