Representing topics labels for exploring digital libraries

Topic models have been shown to be a useful way of representing the content of large document collections, for example via visualisation interfaces (topic browsers). These systems enable users to explore collections by way of latent topics. A standard way to represent a topic is using a set of keywords, i.e. the top-n words with highest marginal probability within the topic. However, alternative topic representations have been proposed, including textual and image labels. In this paper, we compare different topic representations, i.e. sets of topic words, textual phrases and images, in a document retrieval task. We asked participants to retrieve relevant documents based on pre-defined queries within a fixed time limit, presenting topics in one of the following modalities: (1) sets of keywords, (2) textual labels, and (3) image labels. Our results show that textual labels are easier for users to interpret than keywords and image labels. Moreover, the precision of retrieved documents for textual and image labels is comparable to the precision achieved by representing topics using sets of keywords, demonstrating that labelling methods are an effective alternative topic representation.