Task: Summarization
Input (English): 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.
Output (English): We generate Wikipedia articles abstractively conditioned on source document text.