  We show that a simple unsupervised masking objective can approach near
supervised performance on abstractive multi-document news summarization. Our
method trains a state-of-the-art neural summarization model to predict the
masked out source document with highest lexical centrality relative to the
multi-document group. In experiments on the Multi-News dataset, our masked
training objective yields a system that outperforms past unsupervised methods
and, in human evaluation, surpasses the best supervised method without
requiring access to any ground-truth summaries. Further, we evaluate how
different measures of lexical centrality, inspired by past work on extractive
summarization, affect final performance.
