High dimensional unstructured data comes with the great trouble of organizing, querying, and information retrieval. If we can learn how to extract latent thematic structure in a text document or a collection of such documents, we can harness the wealth of information that can be retrieved; something that would not have been feasible without the advancements in natural language processing methodologies. In this chapter, we will learn about topic modeling and text summarization. We will learn how to extract hidden themes from documents and collections in order to be able to effectively use it for dozens of purposes such as corpus summarization, document organization, document classification, taxonomy generation of web documents, organizing search engine query results, news or article recommendation systems, and duplicate content detection. We will also discuss an interesting application of probabilistic language models in sentence completion:
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