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        <title>Extract, Select and Rewrite: A Modular Sentence Summarization Method</title>
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        <namePart type="given">Shuo</namePart>
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            <title>Proceedings of the 4th New Frontiers in Summarization Workshop</title>
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            <namePart type="given">Yue</namePart>
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    <abstract>A modular approach has the advantage of being compositional and controllable, comparing to most end-to-end models. In this paper we propose Extract-Select-Rewrite (ESR), a three-phase abstractive sentence summarization method. We decompose summarization into three stages: (i) knowledge extraction, where we extract relation triples from the text using off-the-shelf tools; (ii) content selection, where a subset of triples are selected; and (iii) rewriting, where the selected triple are realized into natural language. Our results demonstrates that ESR is competitive with the best end-to-end models while being more faithful. %than these baseline models. Being modular, ESR‘s modules can be trained on separate data which is beneficial in low-resource settings and enhancing the style controllability on text generation.</abstract>
    <identifier type="citekey">guan-padmakumar-2023-extract</identifier>
    <identifier type="doi">10.18653/v1/2023.newsum-1.4</identifier>
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%0 Conference Proceedings
%T Extract, Select and Rewrite: A Modular Sentence Summarization Method
%A Guan, Shuo
%A Padmakumar, Vishakh
%Y Dong, Yue
%Y Xiao, Wen
%Y Wang, Lu
%Y Liu, Fei
%Y Carenini, Giuseppe
%S Proceedings of the 4th New Frontiers in Summarization Workshop
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F guan-padmakumar-2023-extract
%X A modular approach has the advantage of being compositional and controllable, comparing to most end-to-end models. In this paper we propose Extract-Select-Rewrite (ESR), a three-phase abstractive sentence summarization method. We decompose summarization into three stages: (i) knowledge extraction, where we extract relation triples from the text using off-the-shelf tools; (ii) content selection, where a subset of triples are selected; and (iii) rewriting, where the selected triple are realized into natural language. Our results demonstrates that ESR is competitive with the best end-to-end models while being more faithful. %than these baseline models. Being modular, ESR‘s modules can be trained on separate data which is beneficial in low-resource settings and enhancing the style controllability on text generation.
%R 10.18653/v1/2023.newsum-1.4
%U https://aclanthology.org/2023.newsum-1.4/
%U https://doi.org/10.18653/v1/2023.newsum-1.4
%P 41-48

Markdown (Informal)

[Extract, Select and Rewrite: A Modular Sentence Summarization Method](https://aclanthology.org/2023.newsum-1.4/) (Guan & Padmakumar, NewSum 2023)

• Extract, Select and Rewrite: A Modular Sentence Summarization Method (Guan & Padmakumar, NewSum 2023)

ACL

• Shuo Guan and Vishakh Padmakumar. 2023. Extract, Select and Rewrite: A Modular Sentence Summarization Method. In Proceedings of the 4th New Frontiers in Summarization Workshop, pages 41–48, Singapore. Association for Computational Linguistics.