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        <title>Abstract Meaning Representation for Multi-Document Summarization</title>
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    <name type="personal">
        <namePart type="given">Kexin</namePart>
        <namePart type="family">Liao</namePart>
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    <name type="personal">
        <namePart type="given">Logan</namePart>
        <namePart type="family">Lebanoff</namePart>
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        <namePart type="given">Fei</namePart>
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            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
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            <namePart type="given">Emily</namePart>
            <namePart type="given">M</namePart>
            <namePart type="family">Bender</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <namePart type="given">Leon</namePart>
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            <namePart type="given">Pierre</namePart>
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    <abstract>Generating an abstract from a collection of documents is a desirable capability for many real-world applications. However, abstractive approaches to multi-document summarization have not been thoroughly investigated. This paper studies the feasibility of using Abstract Meaning Representation (AMR), a semantic representation of natural language grounded in linguistic theory, as a form of content representation. Our approach condenses source documents to a set of summary graphs following the AMR formalism. The summary graphs are then transformed to a set of summary sentences in a surface realization step. The framework is fully data-driven and flexible. Each component can be optimized independently using small-scale, in-domain training data. We perform experiments on benchmark summarization datasets and report promising results. We also describe opportunities and challenges for advancing this line of research.</abstract>
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        <date>2018-08</date>
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%0 Conference Proceedings
%T Abstract Meaning Representation for Multi-Document Summarization
%A Liao, Kexin
%A Lebanoff, Logan
%A Liu, Fei
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F liao-etal-2018-abstract
%X Generating an abstract from a collection of documents is a desirable capability for many real-world applications. However, abstractive approaches to multi-document summarization have not been thoroughly investigated. This paper studies the feasibility of using Abstract Meaning Representation (AMR), a semantic representation of natural language grounded in linguistic theory, as a form of content representation. Our approach condenses source documents to a set of summary graphs following the AMR formalism. The summary graphs are then transformed to a set of summary sentences in a surface realization step. The framework is fully data-driven and flexible. Each component can be optimized independently using small-scale, in-domain training data. We perform experiments on benchmark summarization datasets and report promising results. We also describe opportunities and challenges for advancing this line of research.
%U https://aclanthology.org/C18-1101/
%P 1178-1190

Markdown (Informal)

[Abstract Meaning Representation for Multi-Document Summarization](https://aclanthology.org/C18-1101/) (Liao et al., COLING 2018)

• Abstract Meaning Representation for Multi-Document Summarization (Liao et al., COLING 2018)

ACL

• Kexin Liao, Logan Lebanoff, and Fei Liu. 2018. Abstract Meaning Representation for Multi-Document Summarization. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1178–1190, Santa Fe, New Mexico, USA. Association for Computational Linguistics.