  The essential task of Topic Detection and Tracking (TDT) is to organize a
collection of news media into clusters of stories that pertain to the same
real-world event. To apply TDT models to practical applications such as search
engines and discovery tools, human guidance is needed to pin down the scope of
an "event" for the corpus of interest. In this work in progress, we explore a
human-in-the-loop method that helps users iteratively fine-tune TDT algorithms
so that both the algorithms and the users themselves better understand the
nature of the events. We generate a visual overview of the entire corpus,
allowing the user to select regions of interest from the overview, and then ask
a series of questions to affirm (or reject) that the selected documents belong
to the same event. The answers to these questions supplement the training data
for the event similarity model that underlies the system.
