  Topic Modelling is one of the most prevalent text analysis technique used to
explore and retrieve collection of documents. The evaluation of the topic model
algorithms is still a very challenging tasks due to the absence of
gold-standard list of topics to compare against for every corpus. In this work,
we present a specificity score based on keywords properties that is able to
select the most informative topics. This approach helps the user to focus on
the most informative topics. In the experiments, we show that we are able to
compress the state-of-the-art topic modelling results of different factors with
an information loss that is much lower than the solution based on the recent
coherence score presented in literature.
