Document clustering is a powerful method with numerous applications, where the core idea is to group text into smaller and more manageable pieces of semantically related information. While there has been progress in the research community on improving the quality of clusters, the extraction of information that explains the semantic content of the clusters is still mostly a manual activity, as it requires the inspection of sample documents. In this work, we propose three main cluster explanation approaches that extend the current state of the art, which is mostly based on predicting a label for each cluster (i.e. cluster labelling). The first approach is based on scores extracted from the word distributions; the second is based on augmenting the score-based explanations using external knowledge-bases, and the third one combines the first two methods to exploit the knowledge coming from a labelled dataset. We also discuss the limitations of the current metric presented in the literature and extend it to evaluate our approaches using ground truth. We also provided an extensive set of evaluations to verify the effectiveness of the proposed algorithms, by running multiple experiments to compare our approaches with different baselines. Results indicate that improved explanations are possible and that linking external knowledge can provide more general cluster descriptions. Semi-supervised approaches also demonstrated meaningful insight. User studies were also conducted highlighting that users prefer specialized explanations.