Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model
Table 3: Comparison of our Multi-News dataset to other MDS datasets as well as an SDS dataset used as training data for MDS (CNNDM). Training, validation and testing size splits (article(s) to summary) are provided when applicable. Statistics for multi-document inputs are calculated on the concatenation of all input sources.
['[BOLD] Dataset', '[BOLD] # pairs', '[BOLD] # words (doc)', '[BOLD] # sents (docs)', '[BOLD] # words (summary)', '[BOLD] # sents (summary)', '[BOLD] vocab size']
Table 3 compares Multi-News to other news datasets used in experiments below. We choose to compare Multi-News with DUC data from 2003 and 2004 and TAC 2011 data, which are typically used in multi-document settings. Additionally, we compare to the single-document CNNDM dataset, as this has been recently used in work which adapts SDS to MDS (Lebanoff et al., 2018). The number of examples in our Multi-News dataset is two orders of magnitude larger than previous MDS news data. The total number of words in the concatenated inputs is shorter than other MDS datasets, as those consist of 10 input documents, but larger than SDS datasets, as expected. Our summaries are notably longer than in other works, about 260 words on average.