With the enormous growth rate in user-generated videos, it is becoming increasingly important to be able to navigate them efficiently. Video summarization is considered a promising approach for efficacious realization of video content through Identifying and picking out descriptive frames of the video. In this paper, we propose an adaptive framework called Smart-Trailer (S-Trailer) to automate the process of creating an online trailer for any movie based only on its subtitle. The language used in the subtitle is English. The framework analyzes the movie subtitle file to extract relevant textual features that are used to classify the movie into its corresponding genre(s). Initial experimentation resulted in generating genre-classification corpus. The generated corpus is tested against real movies dataset and showed high classification accuracy rate (0.89) in classifying movies into their corresponding genre(s). The proposed system returned automated trailers that contain on average 47% accuracy in terms of recalling scenes appeared on the original movie trailer for different movie genres. Currently, we employ deep learning techniques to captures user behaviors and opinions in order to adapt our system to provide users with relevant video scenes recommendations that match their preferences.