Learning with fuzzy hypergraphs: a topical approach to query-oriented text summarization
Table 4: Comparison of our MRC algorithm with four methods on DUC05, DUC06 and DUC07.
['Algorithm', 'DUC05 ROUGE-2', 'DUC05 ROUGE-SU4', 'DUC06 ROUGE-2', 'DUC06 ROUGE-SU4', 'DUC07 ROUGE-2', 'DUC07 ROUGE-SU4']
We observe that our MRC algorithm outperforms TS-LexRank and Hubs and Authorities by at least 8% on DUC06 and DUC07 and at least 2% on DUC05 which justifies our use of a hypergraph that incorporates group relationships among sentences rather than a simple graph. HyperSum performs slightly better than MRC on DUC05 in terms of ROUGE-2. However, our method outperforms HyperSum and HERF by at least 5% on DUC06 and DUC07. These two hypergraph approaches are limited to the detection of disjoint sentence clusters and do not take advantage of the fuzzy semantic relationships between sentences. They also fail to provide a proper method of sentence selection after sentence ranking, while our method involves the maximization of Relevance and Topical Coverage.