Online calculation of word-clouds for efficient label summarization

Large amounts of information are available on the Internet in the form of natural language text that can be processed as a stream of documents. Users need solutions that summarize the vast volume of data. Word clouds are a popular graphical representation approach that allows them to obtain such a quick visual summary. Nevertheless, the exact solution to this problem has high memory requirements, and is not scalable as the collection size grows up. In this work, we provide a method for approximate online computation of word clouds to summarize the contents of each label or category of a given text stream, using only the most relevant terms of each document according to some weighting function. We experimentally show that our method, based on sketching techniques, obtains a good performance while using a restricted quantity of memory.