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        <title>Pretraining-Based Natural Language Generation for Text Summarization</title>
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        <namePart type="given">Haoyu</namePart>
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    <name type="personal">
        <namePart type="given">Jianjun</namePart>
        <namePart type="family">Xu</namePart>
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    <abstract>In this paper, we propose a novel pretraining-based encoder-decoder framework, which can generate the output sequence based on the input sequence in a two-stage manner. For the encoder of our model, we encode the input sequence into context representations using BERT. For the decoder, there are two stages in our model, in the first stage, we use a Transformer-based decoder to generate a draft output sequence. In the second stage, we mask each word of the draft sequence and feed it to BERT, then by combining the input sequence and the draft representation generated by BERT, we use a Transformer-based decoder to predict the refined word for each masked position. To the best of our knowledge, our approach is the first method which applies the BERT into text generation tasks. As the first step in this direction, we evaluate our proposed method on the text summarization task. Experimental results show that our model achieves new state-of-the-art on both CNN/Daily Mail and New York Times datasets.</abstract>
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    <identifier type="doi">10.18653/v1/K19-1074</identifier>
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        <url>https://aclanthology.org/K19-1074</url>
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%0 Conference Proceedings
%T Pretraining-Based Natural Language Generation for Text Summarization
%A Zhang, Haoyu
%A Cai, Jingjing
%A Xu, Jianjun
%A Wang, Ji
%Y Bansal, Mohit
%Y Villavicencio, Aline
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F zhang-etal-2019-pretraining
%X In this paper, we propose a novel pretraining-based encoder-decoder framework, which can generate the output sequence based on the input sequence in a two-stage manner. For the encoder of our model, we encode the input sequence into context representations using BERT. For the decoder, there are two stages in our model, in the first stage, we use a Transformer-based decoder to generate a draft output sequence. In the second stage, we mask each word of the draft sequence and feed it to BERT, then by combining the input sequence and the draft representation generated by BERT, we use a Transformer-based decoder to predict the refined word for each masked position. To the best of our knowledge, our approach is the first method which applies the BERT into text generation tasks. As the first step in this direction, we evaluate our proposed method on the text summarization task. Experimental results show that our model achieves new state-of-the-art on both CNN/Daily Mail and New York Times datasets.
%R 10.18653/v1/K19-1074
%U https://aclanthology.org/K19-1074
%U https://doi.org/10.18653/v1/K19-1074
%P 789-797

Markdown (Informal)

[Pretraining-Based Natural Language Generation for Text Summarization](https://aclanthology.org/K19-1074) (Zhang et al., CoNLL 2019)

• Pretraining-Based Natural Language Generation for Text Summarization (Zhang et al., CoNLL 2019)

ACL

• Haoyu Zhang, Jingjing Cai, Jianjun Xu, and Ji Wang. 2019. Pretraining-Based Natural Language Generation for Text Summarization. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 789–797, Hong Kong, China. Association for Computational Linguistics.