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.
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.
Research Areas
