  Real-world event sequences are often complex and heterogeneous, making it
difficult to create meaningful visualizations using simple data aggregation and
visual encoding techniques. Consequently, visualization researchers have
developed numerous visual summarization techniques to generate concise
overviews of sequential data. These techniques vary widely in terms of summary
structures and contents, and currently there is a knowledge gap in
understanding the effectiveness of these techniques. In this work, we present
the design and results of an insight-based crowdsourcing experiment evaluating
three existing visual summarization techniques: CoreFlow, SentenTree, and
Sequence Synopsis. We compare the visual summaries generated by these
techniques across three tasks, on six datasets, at six levels of granularity.
We analyze the effects of these variables on summary quality as rated by
participants and completion time of the experiment tasks. Our analysis shows
that Sequence Synopsis produces the highest-quality visual summaries for all
three tasks, but understanding Sequence Synopsis results also takes the longest
time. We also find that the participants evaluate visual summary quality based
on two aspects: content and interpretability. We discuss the implications of
our findings on developing and evaluating new visual summarization techniques.
