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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Marie-Francine</namePart>
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    <abstract>Recently, Transformer-based models have been proven effective in the abstractive summarization task by creating fluent and informative summaries. Nevertheless, these models still suffer from the short-range dependency problem, causing them to produce summaries that miss the key points of document. In this paper, we attempt to address this issue by introducing a neural topic model empowered with normalizing flow to capture the global semantics of the document, which are then integrated into the summarization model. In addition, to avoid the overwhelming effect of global semantics on contextualized representation, we introduce a mechanism to control the amount of global semantics supplied to the text generation module. Our method outperforms state-of-the-art summarization models on five common text summarization datasets, namely CNN/DailyMail, XSum, Reddit TIFU, arXiv, and PubMed.</abstract>
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%0 Conference Proceedings
%T Enriching and Controlling Global Semantics for Text Summarization
%A Nguyen, Thong
%A Luu, Anh Tuan
%A Lu, Truc
%A Quan, Tho
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F nguyen-etal-2021-enriching
%X Recently, Transformer-based models have been proven effective in the abstractive summarization task by creating fluent and informative summaries. Nevertheless, these models still suffer from the short-range dependency problem, causing them to produce summaries that miss the key points of document. In this paper, we attempt to address this issue by introducing a neural topic model empowered with normalizing flow to capture the global semantics of the document, which are then integrated into the summarization model. In addition, to avoid the overwhelming effect of global semantics on contextualized representation, we introduce a mechanism to control the amount of global semantics supplied to the text generation module. Our method outperforms state-of-the-art summarization models on five common text summarization datasets, namely CNN/DailyMail, XSum, Reddit TIFU, arXiv, and PubMed.
%R 10.18653/v1/2021.emnlp-main.744
%U https://aclanthology.org/2021.emnlp-main.744
%U https://doi.org/10.18653/v1/2021.emnlp-main.744
%P 9443-9456

Markdown (Informal)

[Enriching and Controlling Global Semantics for Text Summarization](https://aclanthology.org/2021.emnlp-main.744) (Nguyen et al., EMNLP 2021)

• Enriching and Controlling Global Semantics for Text Summarization (Nguyen et al., EMNLP 2021)

ACL

• Thong Nguyen, Anh Tuan Luu, Truc Lu, and Tho Quan. 2021. Enriching and Controlling Global Semantics for Text Summarization. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 9443–9456, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.