  Statistical topic models efficiently facilitate the exploration of
large-scale data sets. Many models have been developed and broadly used to
summarize the semantic structure in news, science, social media, and digital
humanities. However, a common and practical objective in data exploration tasks
is not to enumerate all existing topics, but to quickly extract representative
ones that broadly cover the content of the corpus, i.e., a few topics that
serve as a good summary of the data. Most existing topic models fit exactly the
same number of topics as a user specifies, which have imposed an unnecessary
burden to the users who have limited prior knowledge. We instead propose new
models that are able to learn fewer but more representative topics for the
purpose of data summarization. We propose a reinforced random walk that allows
prominent topics to absorb tokens from similar and smaller topics, thus
enhances the diversity among the top topics extracted. With this reinforced
random walk as a general process embedded in classical topic models, we obtain
\textit{diverse topic models} that are able to extract the most prominent and
diverse topics from data. The inference procedures of these diverse topic
models remain as simple and efficient as the classical models. Experimental
results demonstrate that the diverse topic models not only discover topics that
better summarize the data, but also require minimal prior knowledge of the
users.
