Generating Wikipedia by Summarizing Long Sequences

Code

• github.com/tensorflow/tensor2tensor
  OfficialIn papertf★ 17,096
• github.com/aseidelo/wiki_generator
  tf★ 5
• github.com/brsarah20/Alphafold2
  pytorch★ 2
• github.com/lucidrains/memory-compressed-attention
  pytorch★ 0

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.

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Reproductions