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    <titleInfo>
        <title>A Proposition-Based Abstractive Summariser</title>
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    <name type="personal">
        <namePart type="given">Yimai</namePart>
        <namePart type="family">Fang</namePart>
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        <namePart type="given">Haoyue</namePart>
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    <name type="personal">
        <namePart type="given">Ewa</namePart>
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    <name type="personal">
        <namePart type="given">Alexander</namePart>
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    <name type="personal">
        <namePart type="given">Simone</namePart>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
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            <publisher>The COLING 2016 Organizing Committee</publisher>
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    <abstract>Abstractive summarisation is not yet common amongst today’s deployed and research systems. Most existing systems either extract sentences or compress individual sentences. In this paper, we present a summariser that works by a different paradigm. It is a further development of an existing summariser that has an incremental, proposition-based content selection process but lacks a natural language (NL) generator for the final output. Using an NL generator, we can now produce the summary text to directly reflect the selected propositions. Our evaluation compares textual quality of our system to the earlier preliminary output method, and also uses ROUGE to compare to various summarisers that use the traditional method of sentence extraction, followed by compression. Our results suggest that cutting out the middle-man of sentence extraction can lead to better abstractive summaries.</abstract>
    <identifier type="citekey">fang-etal-2016-proposition</identifier>
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        <url>https://aclanthology.org/C16-1055</url>
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        <date>2016-12</date>
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%0 Conference Proceedings
%T A Proposition-Based Abstractive Summariser
%A Fang, Yimai
%A Zhu, Haoyue
%A Muszyńska, Ewa
%A Kuhnle, Alexander
%A Teufel, Simone
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F fang-etal-2016-proposition
%X Abstractive summarisation is not yet common amongst today’s deployed and research systems. Most existing systems either extract sentences or compress individual sentences. In this paper, we present a summariser that works by a different paradigm. It is a further development of an existing summariser that has an incremental, proposition-based content selection process but lacks a natural language (NL) generator for the final output. Using an NL generator, we can now produce the summary text to directly reflect the selected propositions. Our evaluation compares textual quality of our system to the earlier preliminary output method, and also uses ROUGE to compare to various summarisers that use the traditional method of sentence extraction, followed by compression. Our results suggest that cutting out the middle-man of sentence extraction can lead to better abstractive summaries.
%U https://aclanthology.org/C16-1055
%P 567-578

Markdown (Informal)

[A Proposition-Based Abstractive Summariser](https://aclanthology.org/C16-1055) (Fang et al., COLING 2016)

• A Proposition-Based Abstractive Summariser (Fang et al., COLING 2016)

ACL

• Yimai Fang, Haoyue Zhu, Ewa Muszyńska, Alexander Kuhnle, and Simone Teufel. 2016. A Proposition-Based Abstractive Summariser. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 567–578, Osaka, Japan. The COLING 2016 Organizing Committee.