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Generating Wikipedia by Summarizing Long Sequences
Peter J. Liu*, Mohammad Saleh*, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer
Feb 15, 2018 (modified: Feb 28, 2018)ICLR 2018 Conference Blind Submissionreaders: everyoneShow Bibtex
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.
TL;DR:We generate Wikipedia articles abstractively conditioned on source document text.
Keywords:abstractive summarization, Transformer, long sequences, natural language processing, sequence transduction, Wikipedia, extractive summarization
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