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    <titleInfo>
        <title>Sentence-level Planning for Especially Abstractive Summarization</title>
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    <name type="personal">
        <namePart type="given">Andreas</namePart>
        <namePart type="family">Marfurt</namePart>
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            <title>Proceedings of the Third Workshop on New Frontiers in Summarization</title>
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        <name type="personal">
            <namePart type="given">Giuseppe</namePart>
            <namePart type="family">Carenini</namePart>
            <role>
                <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">Yue</namePart>
            <namePart type="family">Dong</namePart>
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        <name type="personal">
            <namePart type="given">Fei</namePart>
            <namePart type="family">Liu</namePart>
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        <name type="personal">
            <namePart type="given">Lu</namePart>
            <namePart type="family">Wang</namePart>
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        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Abstractive summarization models heavily rely on copy mechanisms, such as the pointer network or attention, to achieve good performance, measured by textual overlap with reference summaries. As a result, the generated summaries stay close to the formulations in the source document. We propose the *sentence planner* model to generate more abstractive summaries. It includes a hierarchical decoder that first generates a representation for the next summary sentence, and then conditions the word generator on this representation. Our generated summaries are more abstractive and at the same time achieve high ROUGE scores when compared to human reference summaries. We verify the effectiveness of our design decisions with extensive evaluations.</abstract>
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    <identifier type="doi">10.18653/v1/2021.newsum-1.1</identifier>
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        <url>https://aclanthology.org/2021.newsum-1.1</url>
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        <date>2021-11</date>
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            <start>1</start>
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%0 Conference Proceedings
%T Sentence-level Planning for Especially Abstractive Summarization
%A Marfurt, Andreas
%A Henderson, James
%Y Carenini, Giuseppe
%Y Cheung, Jackie Chi Kit
%Y Dong, Yue
%Y Liu, Fei
%Y Wang, Lu
%S Proceedings of the Third Workshop on New Frontiers in Summarization
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and in Dominican Republic
%F marfurt-henderson-2021-sentence
%X Abstractive summarization models heavily rely on copy mechanisms, such as the pointer network or attention, to achieve good performance, measured by textual overlap with reference summaries. As a result, the generated summaries stay close to the formulations in the source document. We propose the *sentence planner* model to generate more abstractive summaries. It includes a hierarchical decoder that first generates a representation for the next summary sentence, and then conditions the word generator on this representation. Our generated summaries are more abstractive and at the same time achieve high ROUGE scores when compared to human reference summaries. We verify the effectiveness of our design decisions with extensive evaluations.
%R 10.18653/v1/2021.newsum-1.1
%U https://aclanthology.org/2021.newsum-1.1
%U https://doi.org/10.18653/v1/2021.newsum-1.1
%P 1-14

Markdown (Informal)

[Sentence-level Planning for Especially Abstractive Summarization](https://aclanthology.org/2021.newsum-1.1) (Marfurt & Henderson, NewSum 2021)

• Sentence-level Planning for Especially Abstractive Summarization (Marfurt & Henderson, NewSum 2021)

ACL

• Andreas Marfurt and James Henderson. 2021. Sentence-level Planning for Especially Abstractive Summarization. In Proceedings of the Third Workshop on New Frontiers in Summarization, pages 1–14, Online and in Dominican Republic. Association for Computational Linguistics.