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-Unsupervised Video Summarization:
+**Evaluating and Extending Unsupervised VideoSummarization Methods**
+
 1. Evaluation unsupervised methods with different metric under same configuration.
 2. Investigating effect of extracted features in unsupervised methods and extend it to perform better than baseline work.
 3. Identify gaps form last step and try to fill by porposed solution by extending or modifiying an existing model.
 
-Abstract:
+**Abstract**
+
 This study validates the recent work in unsupervised video summarization andextends the experiments with feature variation.
 In  our  understanding,  deeplearning-based approaches select frame-candidates for a video summary.
 Theseframes are drawn with a probability that can be used to calculate the scaled im-portance for comparison with the basic truth as an evaluation.