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        <title>Extractive Summarization Using Multi-Task Learning with Document Classification</title>
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    <name type="personal">
        <namePart type="given">Masaru</namePart>
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    <name type="personal">
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    <abstract>The need for automatic document summarization that can be used for practical applications is increasing rapidly. In this paper, we propose a general framework for summarization that extracts sentences from a document using externally related information. Our work is aimed at single document summarization using small amounts of reference summaries. In particular, we address document summarization in the framework of multi-task learning using curriculum learning for sentence extraction and document classification. The proposed framework enables us to obtain better feature representations to extract sentences from documents. We evaluate our proposed summarization method on two datasets: financial report and news corpus. Experimental results demonstrate that our summarizers achieve performance that is comparable to state-of-the-art systems.</abstract>
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%0 Conference Proceedings
%T Extractive Summarization Using Multi-Task Learning with Document Classification
%A Isonuma, Masaru
%A Fujino, Toru
%A Mori, Junichiro
%A Matsuo, Yutaka
%A Sakata, Ichiro
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F isonuma-etal-2017-extractive
%X The need for automatic document summarization that can be used for practical applications is increasing rapidly. In this paper, we propose a general framework for summarization that extracts sentences from a document using externally related information. Our work is aimed at single document summarization using small amounts of reference summaries. In particular, we address document summarization in the framework of multi-task learning using curriculum learning for sentence extraction and document classification. The proposed framework enables us to obtain better feature representations to extract sentences from documents. We evaluate our proposed summarization method on two datasets: financial report and news corpus. Experimental results demonstrate that our summarizers achieve performance that is comparable to state-of-the-art systems.
%R 10.18653/v1/D17-1223
%U https://aclanthology.org/D17-1223
%U https://doi.org/10.18653/v1/D17-1223
%P 2101-2110

Markdown (Informal)

[Extractive Summarization Using Multi-Task Learning with Document Classification](https://aclanthology.org/D17-1223) (Isonuma et al., EMNLP 2017)

• Extractive Summarization Using Multi-Task Learning with Document Classification (Isonuma et al., EMNLP 2017)

ACL

• Masaru Isonuma, Toru Fujino, Junichiro Mori, Yutaka Matsuo, and Ichiro Sakata. 2017. Extractive Summarization Using Multi-Task Learning with Document Classification. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2101–2110, Copenhagen, Denmark. Association for Computational Linguistics.