  In our work, we propose to represent HTM as a set of flat models, or layers,
and a set of topical hierarchies, or edges. We suggest several quality measures
for edges of hierarchical models, resembling those proposed for flat models. We
conduct an assessment experimentation and show strong correlation between the
proposed measures and human judgement on topical edge quality. We also
introduce heterogeneous algorithm to build hierarchical topic models for
heterogeneous data sources. We show how making certain adjustments to learning
process helps to retain original structure of customized models while allowing
for slight coherent modifications for new documents. We evaluate this approach
using the proposed measures and show that the proposed heterogeneous algorithm
significantly outperforms the baseline concat approach. Finally, we implement
our own ESE called Rysearch, which demonstrates the potential of ARTM approach
for visualizing large heterogeneous document collections.
