Learning with fuzzy hypergraphs: a topical approach to query-oriented text summarization
Table 5: Comparison with DUC05, DUC06 and DUC07 systems
['Method', 'DUC05 ROUGE-2', 'DUC05 ROUGE-SU4', 'DUC06 ROUGE-2', 'DUC06 ROUGE-SU4', 'DUC07 ROUGE-2', 'DUC07 ROUGE-SU4']
Finally, we compare the performance of our method to that of other summarizers submitted for DUC07 summarization tasks. The same results are displayed for DUC06 dataset in which the best systems are S24, S15, S12 and S8, and for DUC05 in which the best systems are S15, S17, S10 and S8. Apart from DUC05, we observe that our proposed method slightly outperforms other summarizers in terms of ROUGE-2 and ROUGE-SU4 but it performs worse than the human summaries which was expected since we merely extract sentences from the original corpus , hence the resulting summary cannot match the quality of abstractive summaries produced by humans. Overall, we observe that our system achieves better performances on DUC06 and DUC07 than it does on DUC05 dataset.