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Automatic video summarization is still an unsolved problem due to severalchallenges. The currently available datasets either have very short videos orhave few long videos of only a particular type. We introduce a new benchmarkingvideo dataset called VISIOCITY (VIdeo SummarIzatiOn based on Continuity, Intentand DiversiTY) which comprises of longer videos across six different categorieswith dense concept annotations capable of supporting different flavors of videosummarization and other vision problems. For long videos, human referencesummaries necessary for supervised video summarization techniques are difficultto obtain. We explore strategies to automatically generate multiple referencesummaries from indirect ground truth present in VISIOCITY. We show that thesesummaries are at par with human summaries. We also present a study of differentdesired characteristics of a good summary and demonstrate how it is normal tohave two good summaries with different characteristics. Thus we argue thatevaluating a summary against one or more human summaries and using a singlemeasure has its shortcomings. We propose an evaluation framework for betterquantitative assessment of summary quality which is closer to human judgment.Lastly, we present insights into how a model can be enhanced to yield bettersummaries. Sepcifically, when multiple diverse ground truth summaries canexist, learning from them individually and using a combination of lossfunctions measuring different characteristics is better than learning from asingle combined (oracle) ground truth summary using a single loss function. Wedemonstrate the effectiveness of doing so as compared to some of therepresentative state of the art techniques tested on VISIOCITY. We releaseVISIOCITY as a benchmarking dataset and invite researchers to test theeffectiveness of their video summarization algorithms on VISIOCITY.

How Good is a Video Summary? A New Benchmarking Dataset and Evaluation Framework Towards Realistic Video Summarization