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        <title>Topic-Guided Abstractive Multi-Document Summarization</title>
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        <namePart type="given">Peng</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>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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    <abstract>A critical point of multi-document summarization (MDS) is to learn the relations among various documents. In this paper, we propose a novel abstractive MDS model, in which we represent multiple documents as a heterogeneous graph, taking semantic nodes of different granularities into account, and then apply a graph-to-sequence framework to generate summaries. Moreover, we employ a neural topic model to jointly discover latent topics that can act as cross-document semantic units to bridge different documents and provide global information to guide the summary generation. Since topic extraction can be viewed as a special type of summarization that “summarizes” texts into a more abstract format, i.e., a topic distribution, we adopt a multi-task learning strategy to jointly train the topic and summarization module, allowing the promotion of each other. Experimental results on the Multi-News dataset demonstrate that our model outperforms previous state-of-the-art MDS models on both Rouge scores and human evaluation, meanwhile learns high-quality topics.</abstract>
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    <identifier type="doi">10.18653/v1/2021.findings-emnlp.126</identifier>
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        <date>2021-11</date>
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            <start>1463</start>
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%0 Conference Proceedings
%T Topic-Guided Abstractive Multi-Document Summarization
%A Cui, Peng
%A Hu, Le
%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 cui-hu-2021-topic-guided
%X A critical point of multi-document summarization (MDS) is to learn the relations among various documents. In this paper, we propose a novel abstractive MDS model, in which we represent multiple documents as a heterogeneous graph, taking semantic nodes of different granularities into account, and then apply a graph-to-sequence framework to generate summaries. Moreover, we employ a neural topic model to jointly discover latent topics that can act as cross-document semantic units to bridge different documents and provide global information to guide the summary generation. Since topic extraction can be viewed as a special type of summarization that “summarizes” texts into a more abstract format, i.e., a topic distribution, we adopt a multi-task learning strategy to jointly train the topic and summarization module, allowing the promotion of each other. Experimental results on the Multi-News dataset demonstrate that our model outperforms previous state-of-the-art MDS models on both Rouge scores and human evaluation, meanwhile learns high-quality topics.
%R 10.18653/v1/2021.findings-emnlp.126
%U https://aclanthology.org/2021.findings-emnlp.126
%U https://doi.org/10.18653/v1/2021.findings-emnlp.126
%P 1463-1472

Markdown (Informal)

[Topic-Guided Abstractive Multi-Document Summarization](https://aclanthology.org/2021.findings-emnlp.126) (Cui & Hu, Findings 2021)

• Topic-Guided Abstractive Multi-Document Summarization (Cui & Hu, Findings 2021)

ACL

• Peng Cui and Le Hu. 2021. Topic-Guided Abstractive Multi-Document Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1463–1472, Punta Cana, Dominican Republic. Association for Computational Linguistics.