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In this paper, we present a system for summarization of scientific and structured documents that has three components: section mixture models are used for estimation of the weights of terms; a hypothesis test to select a subset of these terms; and a sentence extractor based on techniques for combinatorial optimization. The section mixture models approach is an adaptation of a bigram mixture model...
OCCAMS is a new algorithm for the Multi-Document Summarization (MDS) problem. We use Latent Semantic Analysis (LSA) to produce term weights which identify the main theme(s) of a set of documents. These are used by our heuristic for extractive sentence selection which borrows techniques from combinatorial optimization to select a set of sentences such that the combined weight of the terms covered is...
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