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

Existing graph-based methods for extractive document summarization represent sentences of a corpus as the nodes of a graph in which edges depict relationships of lexical similarity between sentences. This approach fails to capture semantic similarities between sentences when they express a similar information but have few words in common and are thus lexically dissimilar. To overcome this issue, we propose to extract semantic similarities based on topical representations of sentences. Inspired by the Hierarchical Dirichlet Process, we propose a topic model to infer topic distributions of sentences. As each topic defines a semantic connection among sentences with a certain degree of membership for each sentence, we propose a fuzzy hypergraph model in which nodes are sentences and fuzzy hyperedges are topics. To produce an informative summary, we extract a set of sentences from the corpus by simultaneously maximizing their relevance to a user-defined query, their centrality in the fuzzy hypergraph and their coverage of topics present in the corpus. We formulate an algorithm building on the theory of submodular functions to solve the associated optimization problem. A thorough comparative analysis with other graph-based summarizers demonstrates the superiority of our method in terms of content coverage of the summaries. (C) 2019 Elsevier Inc. All rights reserved.