We show that generating English Wikipedia articles can be approached as a multi-
 document summarization of source documents. We use extractive summarization
 to coarsely identify salient information and a neural abstractive model to generate
 the article. For the abstractive model, we introduce a decoder-only architecture
 that can scalably attend to very long sequences, much longer than typical encoder-
 decoder architectures used in sequence transduction. We show that this model can
 generate fluent, coherent multi-sentence paragraphs and even whole Wikipedia
 articles. When given reference documents, we show it can extract relevant factual
 information as reflected in perplexity, ROUGE scores and human evaluations.
We generate Wikipedia articles abstractively conditioned on source document text.