  Text corpus exploration (TCE) spans the range of exploratory search tasks: it
goes beyond simple retrieval to include item discovery and learning about the
corpus and topic. Systems support TCE with tools such as similarity-based
recommendations and embedding-based spatial maps. However, these tools address
specific tasks; current systems lack the flexibility to support the range of
tasks encountered in practice and the iterative, multiscale, workflows users
employ. In this paper, we provide methods that enhance TCE tools with post hoc
explanations and multiscale, comparative designs to provide flexible support
for user needs. We introduce salience functions as a mechanism to provide post
hoc explanations of similarity, recommendations, and spatial placement. This
post hoc strategy allows our approach to complement a variety of underlying
algorithms; the salience functions provide both exemplar- and feature-based
explanations at scales ranging from individual documents through to the entire
corpus. These explanations are incorporated into a set of views that operate at
multiple scales. The views use design elements that explicitly support
comparison to enable flexible integration. Together, these form an approach
that provides a flexible toolset that can address a range of tasks. We
demonstrate our approach in a prototype system that enables the exploration of
corpora of paper abstracts and newspaper archives. Examples illustrate how our
approach enables the system to flexibly support a wide range of tasks and
workflows that emerge in user scenarios. A user study confirms that researchers
are able to use our system to achieve a variety of tasks.
