Visually Summarizing Semantic Evolution in Document Streams with Topic Table

We propose a visualization technique for summarizing contents of document streams, such as news or scientific archives. The content of streaming documents change over time and so do themes the documents are about. Topic evolution is a relatively new research subject that encompasses the unsupervised discovery of thematic subjects in a document collection

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the adaptation of these subjects as new documents arrive. While many powerful topic evolution methods exist, the combination of learning

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visualization of the evolving topics has been less explored, although it is indispensable for understanding a dynamic document collection.

We propose Topic Table, a visualization technique that builds upon topic modeling for deriving a condensed representation of a document collection. Topic Table captures important and intuitively comprehensible aspects of a topic over time: the importance of the topic within the collection, the words characterizing this topic, the semantic changes of a topic from one timepoint to the next. As an example, we visualize content of the NIPS proceedings from 1987 to 1999.