# Multimodal Summarization of User-Generated Videos
## 2 Related Work
### Related Data Sets  
As has already been mentioned, in this work we aim to automatically generate summaries from user-generated videos, mostly from action and extreme sports. Therefore, at the following we attempt to present recent, publicly available data sets, for related video summarization tasks.  
The \"MED Summaries\" [25] is a new dataset for evaluation of dynamic video summaries, containing annotations of 160 videos in total, with ten event categories in the test set. Indicative categories are \"birthday party\", \"changing a vehicle tire\", \"flash mob gathering\", \"getting a vehicle unstuck\", \"grooming an animal\", and so forth. The \"TVSum\" (Title-based Video Summarization) dataset [28] aims to solve the challenging task of prior knowledge in the main topic of the video. The entire dataset consists of 50 videos of various genres (e.g., \"news\", \"how-to\", \"documentary\", \"vlog\", \"egocentric\") and 1000 annotations of shot-level importance scores obtained via crowd-sourcing (20 per video), while video duration ranges between 2 and 10 min. The video and annotation data permit an automatic evaluation of various video summarization techniques, without having to conduct an (expensive) user study. The \"SumMe\" [29] is a video summarization dataset consisting of 25 videos, covering holidays, events and sports, downloaded from the popular platform of YouTube, each annotated with at least 15 human-created summaries (390 in total), while the length of the videos ranges from 1 to 6 min. The \"UT Ego\" (Univ. of Texas at Austin Egocentric) Dataset [30] contains 10 (4 out of 10 are available due to privacy reasons) videos captured from head-mounted cameras on a variety of activities such as \"eating\", \"shopping\", \"attending a lecture\", \"driving\", and \"cooking\". Each video is about 3-5 h long, captured at 15 fps and at 320 \(\times\) 480 resolution uncontrolled setting. Therefore, videos contain shots with fast motion. Finally, the VSUMM dataset [31] has been initially used to produce a static video summary, by a novel evaluation method, able to remove the subjectivity of the summary quality by allowing objective comparisons of methodology between different approaches. This dataset, also known as \"YouTube Dataset\" consists of 50 videos from the Open Video Project ([[http://www.open-video.org/](http://www.open-video.org/)]([http://www.open-video.org/](http://www.open-video.org/))). The duration of the videos varies from 1 to 4 min while the approximately duration of the videos in total is approx. 75 min. The videos originate from a variety of genres such as documentary, educational, ephemeral, historical and lecture. There exist 250 user summaries createdmanually by 50 individuals, each one annotating five videos, that is, each video has five video summaries created by five different users.  
However, in all of the aforementioned cases, the datasets are either not sufficiently large or they are from a wider domain, that is, they are not explicitly user-generated data. Therefore, in this work we also aim to compile a well-defined user-generated dataset to evaluate for training the proposed methodology.