  Automatic generation of summaries from multiple news articles is a valuable
tool as the number of online publications grows rapidly. Single document
summarization (SDS) systems have benefited from advances in neural
encoder-decoder model thanks to the availability of large datasets. However,
multi-document summarization (MDS) of news articles has been limited to
datasets of a couple of hundred examples. In this paper, we introduce
Multi-News, the first large-scale MDS news dataset. Additionally, we propose an
end-to-end model which incorporates a traditional extractive summarization
model with a standard SDS model and achieves competitive results on MDS
datasets. We benchmark several methods on Multi-News and release our data and
code in hope that this work will promote advances in summarization in the
multi-document setting.
