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Table 3: 

Training set statistics comparisons against previous aspect-based summarization datasets. For multi-domain datasets, the sum of all the examples are reported. #Asp./Ex. represents the average number of aspects that a model has to summarize on each example. (*Review saliency is treated as aspects. #Asp. represents the number of aspects per domain if the number of domains is more than one. Compared datasets are the work of Angelidis and Lapata (2018); Yang et al. (2018); Wang and Ling (2016); Frermann and Klementiev (2019), respectively.

DatasetDomain#Dom.#TrainDoc. LengthSum. Length#Asp.#Asp./Ex.
OpoSum Product Review 359,048 138 49 2.00 
Amazon Product Review 240,000 82 − − − 
RottenTomatoes Movie Review 2,458 2369 24 *2 *1.00 
MA-News News 284,701 1350 54 2.98 
 
WikiAsp Encyclopedia 20 320,272 13,672 213 10 1.77 
DatasetDomain#Dom.#TrainDoc. LengthSum. Length#Asp.#Asp./Ex.
OpoSum Product Review 359,048 138 49 2.00 
Amazon Product Review 240,000 82 − − − 
RottenTomatoes Movie Review 2,458 2369 24 *2 *1.00 
MA-News News 284,701 1350 54 2.98 
 
WikiAsp Encyclopedia 20 320,272 13,672 213 10 1.77 
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