19. The computer program product of claim 15 , wherein the second unit of text data is a query.
15. A computer program product for summarizing a first unit of text data with relation to the contents of multiple documents in an existing document collection, the computer program product including a computer-readable medium encoded with computer program instructions, wherein the computer program instructions, when executed by a processor, cause the processor to perform predetermined operations comprising: creating a subspace for the existing document collection without first posting a query, an input involving latent semantic indexing; performing one of a domain driven text summarization, an example type query driven text summarization, and a term type query driven text summarization on a selected document; recomposing a vector using a projection in the subspace representing the contents of multiple documents in the existing document collection when performing the domain driven text summarization or the example type query driven text summarization; computing term relationships representing similarities between query terms and the contents of multiple documents in the existing document collection using a term-term matrix associated with an original term space when performing the term type query driven text summarization; computing a term weight that is representative of the relevance of a term to a second unit of text data with relation to the contents of multiple documents in the document collection, the computing of the term weight including generation of the subspace using the document collection for projection of the text data into the subspace and back into term space in order to get weights for all the terms in the document collection; comparing the computed term weight to a predetermined threshold; returning a relevant term based at least in part on a result of the comparison; summing a plurality of relevant term weights based on a number of occurrences of a plurality of corresponding relevant terms in a segment of the first unit of text data; comparing a plurality of summations based on a plurality of corresponding segments of the first unit of text data to identify a text summarization segment; and returning the text summarization segment.