title: Generating Wikipedia by Summarizing Long Sequences
abstract: 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.
authors: Peter J. Liu* Mohammad Saleh* Etienne Pot Ben Goodrich Ryan Sepassi Lukasz Kaiser Noam Shazeer
hIndex: Noam Shazeer 30