Automatic labelling of topics via analysis of user summaries

Topic models have been widely used to discover useful structures in large collections of documents. A challenge in applying topic models to any text analysis task is to meaningfully label the discovered topics so that users can interpret them. In existing studies, words and bigram phrases extracted internally from documents are used as candidate labels but are not always understandable to humans. In this paper, we propose a novel approach to extracting words and meaningful phrases from external user generated summaries as candidate labels and then rank them via the Kullback-Leibler semantic distance metric. We further apply our approach to analyse an Australian healthcare discussion forum. User study results show that our proposed approach produces meaningful labels for topics and outperforms state-of-the-art approaches to labelling topics.