  In this paper we introduce a new dataset for 360-degree video summarization:
the transformation of 360-degree video content to concise 2D-video summaries
that can be consumed via traditional devices, such as TV sets and smartphones.
The dataset includes ground-truth human-generated summaries, that can be used
for training and objectively evaluating 360-degree video summarization methods.
Using this dataset, we train and assess two state-of-the-art summarization
methods that were originally proposed for 2D-video summarization, to serve as a
baseline for future comparisons with summarization methods that are
specifically tailored to 360-degree video. Finally, we present an interactive
tool that was developed to facilitate the data annotation process and can
assist other annotation activities that rely on video fragment selection.
