SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents
Table 4: Performance of various models on the DUC 2002 set using the limited length recall variants of Rouge at 75 words. SummaRuNNer is statistically within the margin of error at 95% C.I. with respect to Cheng et al ‘16, but both are lower than state-of-the-art results.
['[EMPTY]', 'Rouge-1', 'Rouge-2', 'Rouge-L']
URANK perform very well on the domain they are trained on, but may suffer from domain adaptation issues when tested on a different corpus such as DUC 2002. Graph based unsupervised approaches, on the other hand, may be more robust to domain variations.