{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/generating-wikipedia-by-summarizing-long","title":"Generating Wikipedia by Summarizing Long Sequences","arxiv_id":"1801.10198","date":"2018-01-30","proceeding":"ICLR 2018 1","authors":["Peter J. Liu","Mohammad Saleh","Etienne Pot","Ben Goodrich","Ryan Sepassi","Lukasz Kaiser","Noam Shazeer"],"abstract":"We show that generating English Wikipedia articles can be approached as a\nmulti- document summarization of source documents. We use extractive\nsummarization to coarsely identify salient information and a neural abstractive\nmodel to generate the article. For the abstractive model, we introduce a\ndecoder-only architecture that can scalably attend to very long sequences, much\nlonger than typical encoder- decoder architectures used in sequence\ntransduction. We show that this model can generate fluent, coherent\nmulti-sentence paragraphs and even whole Wikipedia articles. When given\nreference documents, we show it can extract relevant factual information as\nreflected in perplexity, ROUGE scores and human evaluations.","url_abs":"http://arxiv.org/abs/1801.10198v1","url_pdf":"http://arxiv.org/pdf/1801.10198v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"generating-wikipedia-by-summarizing-long","repo_url":"https://github.com/tensorflow/tensor2tensor","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"generating-wikipedia-by-summarizing-long","repo_url":"https://github.com/aseidelo/wiki_generator","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"generating-wikipedia-by-summarizing-long","repo_url":"https://github.com/brsarah20/Alphafold2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"generating-wikipedia-by-summarizing-long","repo_url":"https://github.com/lucidrains/memory-compressed-attention","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"document-summarization","task_name":"Document Summarization"},{"task_slug":"extractive-summarization","task_name":"Extractive Summarization"},{"task_slug":"multi-document-summarization","task_name":"Multi-Document Summarization"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[{"method_slug":"t-d","method_name":"T-D"}],"datasets_introduced":[{"slug":"wikisum","name":"WikiSum","full_name":"WikiSum"},{"slug":"wikipedia-generation","name":"Wikipedia Generation","full_name":"Wikipedia Generation"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1801.10198","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}