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    <titleInfo>
        <title>GEMINI: Controlling The Sentence-Level Summary Style in Abstractive Text Summarization</title>
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    <name type="personal">
        <namePart type="given">Guangsheng</namePart>
        <namePart type="family">Bao</namePart>
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    <name type="personal">
        <namePart type="given">Zebin</namePart>
        <namePart type="family">Ou</namePart>
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    <name type="personal">
        <namePart type="given">Yue</namePart>
        <namePart type="family">Zhang</namePart>
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        <dateIssued>2023-12</dateIssued>
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            <title>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Juan</namePart>
            <namePart type="family">Pino</namePart>
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        <name type="personal">
            <namePart type="given">Kalika</namePart>
            <namePart type="family">Bali</namePart>
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    <abstract>Human experts write summaries using different techniques, including extracting a sentence from the document and rewriting it, or fusing various information from the document to abstract it. These techniques are flexible and thus difficult to be imitated by any single method. To address this issue, we propose an adaptive model, GEMINI, that integrates a rewriter and a generator to mimic the sentence rewriting and abstracting techniques, respectively. GEMINI adaptively chooses to rewrite a specific document sentence or generate a summary sentence from scratch. Experiments demonstrate that our adaptive approach outperforms the pure abstractive and rewriting baselines on three benchmark datasets, achieving the best results on WikiHow. Interestingly, empirical results show that the human summary styles of summary sentences are consistently predictable given their context. We release our code and model at https://github.com/baoguangsheng/gemini.</abstract>
    <identifier type="citekey">bao-etal-2023-gemini</identifier>
    <identifier type="doi">10.18653/v1/2023.emnlp-main.53</identifier>
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        <url>https://aclanthology.org/2023.emnlp-main.53</url>
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    <part>
        <date>2023-12</date>
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            <start>831</start>
            <end>842</end>
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%0 Conference Proceedings
%T GEMINI: Controlling The Sentence-Level Summary Style in Abstractive Text Summarization
%A Bao, Guangsheng
%A Ou, Zebin
%A Zhang, Yue
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F bao-etal-2023-gemini
%X Human experts write summaries using different techniques, including extracting a sentence from the document and rewriting it, or fusing various information from the document to abstract it. These techniques are flexible and thus difficult to be imitated by any single method. To address this issue, we propose an adaptive model, GEMINI, that integrates a rewriter and a generator to mimic the sentence rewriting and abstracting techniques, respectively. GEMINI adaptively chooses to rewrite a specific document sentence or generate a summary sentence from scratch. Experiments demonstrate that our adaptive approach outperforms the pure abstractive and rewriting baselines on three benchmark datasets, achieving the best results on WikiHow. Interestingly, empirical results show that the human summary styles of summary sentences are consistently predictable given their context. We release our code and model at https://github.com/baoguangsheng/gemini.
%R 10.18653/v1/2023.emnlp-main.53
%U https://aclanthology.org/2023.emnlp-main.53
%U https://doi.org/10.18653/v1/2023.emnlp-main.53
%P 831-842

Markdown (Informal)

[GEMINI: Controlling The Sentence-Level Summary Style in Abstractive Text Summarization](https://aclanthology.org/2023.emnlp-main.53) (Bao et al., EMNLP 2023)

• GEMINI: Controlling The Sentence-Level Summary Style in Abstractive Text Summarization (Bao et al., EMNLP 2023)

ACL

• Guangsheng Bao, Zebin Ou, and Yue Zhang. 2023. GEMINI: Controlling The Sentence-Level Summary Style in Abstractive Text Summarization. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 831–842, Singapore. Association for Computational Linguistics.