  Topic models are widely used unsupervised models capable of learning topics -
weighted lists of words and documents - from large collections of text
documents. When topic models are used for discovery of topics in text
collections, a question that arises naturally is how well the model-induced
topics correspond to topics of interest to the analyst. In this paper we
revisit and extend a so far neglected approach to topic model evaluation based
on measuring topic coverage - computationally matching model topics with a set
of reference topics that models are expected to uncover. The approach is well
suited for analyzing models' performance in topic discovery and for large-scale
analysis of both topic models and measures of model quality. We propose new
measures of coverage and evaluate, in a series of experiments, different types
of topic models on two distinct text domains for which interest for topic
discovery exists. The experiments include evaluation of model quality, analysis
of coverage of distinct topic categories, and the analysis of the relationship
between coverage and other methods of topic model evaluation. The paper
contributes a new supervised measure of coverage, and the first unsupervised
measure of coverage. The supervised measure achieves topic matching accuracy
close to human agreement. The unsupervised measure correlates highly with the
supervised one (Spearman's $\rho \geq 0.95$). Other contributions include
insights into both topic models and different methods of model evaluation, and
the datasets and code for facilitating future research on topic coverage.
