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    <titleInfo>
        <title>An Editorial Network for Enhanced Document Summarization</title>
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    <name type="personal">
        <namePart type="given">Edward</namePart>
        <namePart type="family">Moroshko</namePart>
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        <namePart type="given">Guy</namePart>
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    <name type="personal">
        <namePart type="given">David</namePart>
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            <title>Proceedings of the 2nd Workshop on New Frontiers in Summarization</title>
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        <name type="personal">
            <namePart type="given">Lu</namePart>
            <namePart type="family">Wang</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Jackie</namePart>
            <namePart type="given">Chi</namePart>
            <namePart type="given">Kit</namePart>
            <namePart type="family">Cheung</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Giuseppe</namePart>
            <namePart type="family">Carenini</namePart>
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        <name type="personal">
            <namePart type="given">Fei</namePart>
            <namePart type="family">Liu</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>We suggest a new idea of Editorial Network – a mixed extractive-abstractive summarization approach, which is applied as a post-processing step over a given sequence of extracted sentences. We further suggest an effective way for training the “editor” based on a novel soft-labeling approach. Using the CNN/DailyMail dataset we demonstrate the effectiveness of our approach compared to state-of-the-art extractive-only or abstractive-only baselines.</abstract>
    <identifier type="citekey">moroshko-etal-2019-editorial</identifier>
    <identifier type="doi">10.18653/v1/D19-5407</identifier>
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        <date>2019-11</date>
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            <start>57</start>
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%0 Conference Proceedings
%T An Editorial Network for Enhanced Document Summarization
%A Moroshko, Edward
%A Feigenblat, Guy
%A Roitman, Haggai
%A Konopnicki, David
%Y Wang, Lu
%Y Cheung, Jackie Chi Kit
%Y Carenini, Giuseppe
%Y Liu, Fei
%S Proceedings of the 2nd Workshop on New Frontiers in Summarization
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F moroshko-etal-2019-editorial
%X We suggest a new idea of Editorial Network – a mixed extractive-abstractive summarization approach, which is applied as a post-processing step over a given sequence of extracted sentences. We further suggest an effective way for training the “editor” based on a novel soft-labeling approach. Using the CNN/DailyMail dataset we demonstrate the effectiveness of our approach compared to state-of-the-art extractive-only or abstractive-only baselines.
%R 10.18653/v1/D19-5407
%U https://aclanthology.org/D19-5407
%U https://doi.org/10.18653/v1/D19-5407
%P 57-63

Markdown (Informal)

[An Editorial Network for Enhanced Document Summarization](https://aclanthology.org/D19-5407) (Moroshko et al., 2019)

• An Editorial Network for Enhanced Document Summarization (Moroshko et al., 2019)

ACL

• Edward Moroshko, Guy Feigenblat, Haggai Roitman, and David Konopnicki. 2019. An Editorial Network for Enhanced Document Summarization. In Proceedings of the 2nd Workshop on New Frontiers in Summarization, pages 57–63, Hong Kong, China. Association for Computational Linguistics.