{"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/pretraining-based-natural-language-generation","title":"Pretraining-Based Natural Language Generation for Text Summarization","arxiv_id":"1902.09243","date":"2019-02-25","proceeding":"CONLL 2019 11","authors":["Haoyu Zhang","Jianjun Xu","Ji Wang"],"abstract":"In this paper, we propose a novel pretraining-based encoder-decoder\nframework, which can generate the output sequence based on the input sequence\nin a two-stage manner. For the encoder of our model, we encode the input\nsequence into context representations using BERT. For the decoder, there are\ntwo stages in our model, in the first stage, we use a Transformer-based decoder\nto generate a draft output sequence. In the second stage, we mask each word of\nthe draft sequence and feed it to BERT, then by combining the input sequence\nand the draft representation generated by BERT, we use a Transformer-based\ndecoder to predict the refined word for each masked position. To the best of\nour knowledge, our approach is the first method which applies the BERT into\ntext generation tasks. As the first step in this direction, we evaluate our\nproposed method on the text summarization task. Experimental results show that\nour model achieves new state-of-the-art on both CNN/Daily Mail and New York\nTimes datasets.","url_abs":"http://arxiv.org/abs/1902.09243v2","url_pdf":"http://arxiv.org/pdf/1902.09243v2.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":"pretraining-based-natural-language-generation","repo_url":"https://github.com/nayeon7lee/bert-summarization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"pretraining-based-natural-language-generation","repo_url":"https://github.com/praveenjune17/BERT_text_summarisation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"pretraining-based-natural-language-generation","repo_url":"https://github.com/raufer/bert-summarization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"pretraining-based-natural-language-generation","repo_url":"https://github.com/yahah100/text_summarization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"abstractive-text-summarization","task_name":"Abstractive Text Summarization"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"text-generation","task_name":"Text Generation"},{"task_slug":"text-summarization","task_name":"Text Summarization"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bert","method_name":"BERT"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-linear-decay","method_name":"Linear Warmup With Linear Decay"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"wordpiece","method_name":"WordPiece"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/abstractive-text-summarization-on-cnn-daily","task":"Abstractive Text Summarization","dataset":"CNN / Daily Mail","model":"Two-Stage + RL","rank_in_archive_order":30,"of":53,"metrics":{"ROUGE-1":"41.71","ROUGE-2":"19.49","ROUGE-L":"38.79"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1902.09243","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.09243"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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