Exploring hypergraph-based semi-supervised ranking for query-oriented summarization

Traditional graph based sentence ranking algorithms such as LexRank and HITS model the documents to be summarized as a text graph where nodes represent sentences and edges represent pairwise relations. Such modeling cannot capture complex group relationship shared among multiple sentences which can be useful for sentence ranking. In this paper, we propose to take advantage of hypergraph to remedy this defect. In a text hypergraph, nodes still represent sentences, yet hyperedges are allowed to connect more than two sentences. With a text hypergraph, we are thus able to integrate both group relationship and pairwise relationship into a unified framework. Then, a hypergraph based semi-supervised sentence ranking algorithm is developed for query-oriented extractive summarization, where the influence of query is propagated to sentences through the structure of the constructed text hypergraph. When evaluated on DUC datasets, performance of our proposed approach shows improvements compared to a number of baseline systems.