Approaches to document summarization using semantic graphs

Most of the work involves triplet extraction from documents using various tools and then performing co-reference resolution, anaphora resolution and semantic normalization. Finally the refined triplets are formed into a semantic graph. Later a SVM classifier is trained in order to get only the most relevant triplets for summarization purposes. To do this triplets are assigned a set of attributes like, linguistic attributes: the triplet type – subject, verb or object ...
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