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    <titleInfo>
        <title>Post-Editing Extractive Summaries by Definiteness Prediction</title>
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    <name type="personal">
        <namePart type="given">Jad</namePart>
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2021</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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    <abstract>Extractive summarization has been the mainstay of automatic summarization for decades. Despite all the progress, extractive summarizers still suffer from shortcomings including coreference issues arising from extracting sentences away from their original context in the source document. This affects the coherence and readability of extractive summaries. In this work, we propose a lightweight post-editing step for extractive summaries that centers around a single linguistic decision: the definiteness of noun phrases. We conduct human evaluation studies that show that human expert judges substantially prefer the output of our proposed system over the original summaries. Moreover, based on an automatic evaluation study, we provide evidence for our system‘s ability to generate linguistic decisions that lead to improved extractive summaries. We also draw insights about how the automatic system is exploiting some local cues related to the writing style of the main article texts or summary texts to make the decisions, rather than reasoning about the contexts pragmatically.</abstract>
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    <identifier type="doi">10.18653/v1/2021.findings-emnlp.312</identifier>
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        <date>2021-11</date>
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%0 Conference Proceedings
%T Post-Editing Extractive Summaries by Definiteness Prediction
%A Kabbara, Jad
%A Cheung, Jackie Chi Kit
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F kabbara-cheung-2021-post-editing
%X Extractive summarization has been the mainstay of automatic summarization for decades. Despite all the progress, extractive summarizers still suffer from shortcomings including coreference issues arising from extracting sentences away from their original context in the source document. This affects the coherence and readability of extractive summaries. In this work, we propose a lightweight post-editing step for extractive summaries that centers around a single linguistic decision: the definiteness of noun phrases. We conduct human evaluation studies that show that human expert judges substantially prefer the output of our proposed system over the original summaries. Moreover, based on an automatic evaluation study, we provide evidence for our system‘s ability to generate linguistic decisions that lead to improved extractive summaries. We also draw insights about how the automatic system is exploiting some local cues related to the writing style of the main article texts or summary texts to make the decisions, rather than reasoning about the contexts pragmatically.
%R 10.18653/v1/2021.findings-emnlp.312
%U https://aclanthology.org/2021.findings-emnlp.312/
%U https://doi.org/10.18653/v1/2021.findings-emnlp.312
%P 3682-3692

Markdown (Informal)

[Post-Editing Extractive Summaries by Definiteness Prediction](https://aclanthology.org/2021.findings-emnlp.312/) (Kabbara & Cheung, Findings 2021)

• Post-Editing Extractive Summaries by Definiteness Prediction (Kabbara & Cheung, Findings 2021)

ACL

• Jad Kabbara and Jackie Chi Kit Cheung. 2021. Post-Editing Extractive Summaries by Definiteness Prediction. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3682–3692, Punta Cana, Dominican Republic. Association for Computational Linguistics.