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Existing graph-based methods for extractive document summarization representsentences of a corpus as the nodes of a graph or a hypergraph in which edgesdepict relationships of lexical similarity between sentences. Such approachesfail to capture semantic similarities between sentences when they express asimilar information but have few words in common and are thus lexicallydissimilar. To overcome this issue, we propose to extract semantic similaritiesbased on topical representations of sentences. Inspired by the HierarchicalDirichlet Process, we propose a probabilistic topic model in order to infertopic distributions of sentences. As each topic defines a semantic connectionamong a group of sentences with a certain degree of membership for eachsentence, we propose a fuzzy hypergraph model in which nodes are sentences andfuzzy hyperedges are topics. To produce an informative summary, we extract aset of sentences from the corpus by simultaneously maximizing their relevanceto a user-defined query, their centrality in the fuzzy hypergraph and theircoverage of topics present in the corpus. We formulate a polynomial timealgorithm building on the theory of submodular functions to solve theassociated optimization problem. A thorough comparative analysis with othergraph-based summarization systems is included in the paper. Our obtainedresults show the superiority of our method in terms of content coverage of thesummaries.

Learning with fuzzy hypergraphs: a topical approach to query-oriented text summarization