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        <title>Global Optimization under Length Constraint for Neural Text Summarization</title>
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        <namePart type="given">Takuya</namePart>
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        <namePart type="given">Tomoya</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Manabu</namePart>
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            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Anna</namePart>
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    <abstract>We propose a global optimization method under length constraint (GOLC) for neural text summarization models. GOLC increases the probabilities of generating summaries that have high evaluation scores, ROUGE in this paper, within a desired length. We compared GOLC with two optimization methods, a maximum log-likelihood and a minimum risk training, on CNN/Daily Mail and a Japanese single document summarization data set of The Mainichi Shimbun Newspapers. The experimental results show that a state-of-the-art neural summarization model optimized with GOLC generates fewer overlength summaries while maintaining the fastest processing speed; only 6.70% overlength summaries on CNN/Daily and 7.8% on long summary of Mainichi, compared to the approximately 20% to 50% on CNN/Daily Mail and 10% to 30% on Mainichi with the other optimization methods. We also demonstrate the importance of the generation of in-length summaries for post-editing with the dataset Mainich that is created with strict length constraints. The ex- perimental results show approximately 30% to 40% improved post-editing time by use of in-length summaries.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1099</identifier>
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%0 Conference Proceedings
%T Global Optimization under Length Constraint for Neural Text Summarization
%A Makino, Takuya
%A Iwakura, Tomoya
%A Takamura, Hiroya
%A Okumura, Manabu
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F makino-etal-2019-global
%X We propose a global optimization method under length constraint (GOLC) for neural text summarization models. GOLC increases the probabilities of generating summaries that have high evaluation scores, ROUGE in this paper, within a desired length. We compared GOLC with two optimization methods, a maximum log-likelihood and a minimum risk training, on CNN/Daily Mail and a Japanese single document summarization data set of The Mainichi Shimbun Newspapers. The experimental results show that a state-of-the-art neural summarization model optimized with GOLC generates fewer overlength summaries while maintaining the fastest processing speed; only 6.70% overlength summaries on CNN/Daily and 7.8% on long summary of Mainichi, compared to the approximately 20% to 50% on CNN/Daily Mail and 10% to 30% on Mainichi with the other optimization methods. We also demonstrate the importance of the generation of in-length summaries for post-editing with the dataset Mainich that is created with strict length constraints. The ex- perimental results show approximately 30% to 40% improved post-editing time by use of in-length summaries.
%R 10.18653/v1/P19-1099
%U https://aclanthology.org/P19-1099
%U https://doi.org/10.18653/v1/P19-1099
%P 1039-1048

Markdown (Informal)

[Global Optimization under Length Constraint for Neural Text Summarization](https://aclanthology.org/P19-1099) (Makino et al., ACL 2019)

• Global Optimization under Length Constraint for Neural Text Summarization (Makino et al., ACL 2019)

ACL

• Takuya Makino, Tomoya Iwakura, Hiroya Takamura, and Manabu Okumura. 2019. Global Optimization under Length Constraint for Neural Text Summarization. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1039–1048, Florence, Italy. Association for Computational Linguistics.