SummPip: Unsupervised Multi-Document Summarization with Sentence Graph CompressionThis work has been accepted to SIGIR 2020 for publication.
Table 1: Experiment Results on Multi-News and DUC2004. Bold: Better in the unsupervised setting. Underlined: Best result among both supervised and unsupervised settings. ∗: Statistically significantly better than Centroid with p-value ≤.05.
['Model', 'Multi-News R-1', 'R-2', 'R-SU', 'DUC-2004 R-1', 'R-2', 'R-SU']
Our method produces significantly better results than previous unsupervised methods on Multi-News, including the strong Centroid baseline, and being highly competitive to supervised deep neural models. For DUC-2004, SummPip also exhibits good performance, even though it does not outperform Centroid, which extract the whole sentences. Meanwhile, our method shows very close performance compared to the supervised neural models on both datasets, without any heavy learning process.