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        <title>Abstractive Meeting Summarization: A Survey</title>
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        <namePart type="given">Guokan</namePart>
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    <name type="personal">
        <namePart type="given">Michalis</namePart>
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    <abstract>A system that could reliably identify and sum up the most important points of a conversation would be valuable in a wide variety of real-world contexts, from business meetings to medical consultations to customer service calls. Recent advances in deep learning, and especially the invention of encoder-decoder architectures, has significantly improved language generation systems, opening the door to improved forms of abstractive summarization—a form of summarization particularly well-suited for multi-party conversation. In this paper, we provide an overview of the challenges raised by the task of abstractive meeting summarization and of the data sets, models, and evaluation metrics that have been used to tackle the problems.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00578</identifier>
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        <date>2023</date>
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%0 Journal Article
%T Abstractive Meeting Summarization: A Survey
%A Rennard, Virgile
%A Shang, Guokan
%A Hunter, Julie
%A Vazirgiannis, Michalis
%J Transactions of the Association for Computational Linguistics
%D 2023
%V 11
%I MIT Press
%C Cambridge, MA
%F rennard-etal-2023-abstractive
%X A system that could reliably identify and sum up the most important points of a conversation would be valuable in a wide variety of real-world contexts, from business meetings to medical consultations to customer service calls. Recent advances in deep learning, and especially the invention of encoder-decoder architectures, has significantly improved language generation systems, opening the door to improved forms of abstractive summarization—a form of summarization particularly well-suited for multi-party conversation. In this paper, we provide an overview of the challenges raised by the task of abstractive meeting summarization and of the data sets, models, and evaluation metrics that have been used to tackle the problems.
%R 10.1162/tacl_a_00578
%U https://aclanthology.org/2023.tacl-1.49
%U https://doi.org/10.1162/tacl_a_00578
%P 861-884

Markdown (Informal)

[Abstractive Meeting Summarization: A Survey](https://aclanthology.org/2023.tacl-1.49) (Rennard et al., TACL 2023)

• Abstractive Meeting Summarization: A Survey (Rennard et al., TACL 2023)

ACL

• Virgile Rennard, Guokan Shang, Julie Hunter, and Michalis Vazirgiannis. 2023. Abstractive Meeting Summarization: A Survey. Transactions of the Association for Computational Linguistics, 11:861–884.