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Enhancing Summarization with Text Classification via Topic Consistency

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Machine Learning and Knowledge Discovery in Databases. Research Track (ECML PKDD 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12977))

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Abstract

The recent success of abstractive summarization is partly due to the availability of large-volume and high-quality human-produced summaries for training, which are extremely expensive to obtain. In this paper, we aim to improve state-of-the-art summarization models by utilizing less expensive text classification data. Specifically, we use an eXtreme Multi-label Text Classification (XMTC) classifier to predict relevant category labels for each input document, and impose topic consistency in the system-produced summary or in the document encoder shared by both the classifier and the summarization model. In other words, we use the classifier to distill the training of the summarization model with respect to topical consistency between the input document and the system-generated summary. Technically, we propose two novel formulations for this objective, namely a multi-task approach, and a policy gradient approach. Our experiments show that both approaches significantly improve a state-of-the-art BART summarization model on the CNNDM and XSum datasets. In addition, we propose a new evaluation metric, CON, that measures the topic consistency between the input document and the summary. We show that CON has high correlation with human judgements and is a good complementary metric to the commonly used ROUGE scores.

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Acknowledgments

We thank all the reviewers for their helpful comments. This work is supported in part by National Science Foundation (NSF) under grant IIS-1546329.

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Authors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, USA

    Jingzhou Liu & Yiming Yang

Authors
  1. Jingzhou Liu
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  2. Yiming Yang
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Correspondence to Jingzhou Liu .

Editor information

Editors and Affiliations

  1. ELLIS - The European Laboratory for Learning and Intelligent Systems, Alicante, Spain

    Nuria Oliver

  2. ETHZ and EPFL, Zürich, Switzerland

    Fernando Pérez-Cruz

  3. Johannes Gutenberg University of Mainz, Mainz, Germany

    Stefan Kramer

  4. École Polytechnique, Palaiseau, France

    Jesse Read

  5. Basque Center for Applied Mathematics, Bilbao, Spain

    Jose A. Lozano

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Liu, J., Yang, Y. (2021). Enhancing Summarization with Text Classification via Topic Consistency. In: Oliver, N., Pérez-Cruz, F., Kramer, S., Read, J., Lozano, J.A. (eds) Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2021. Lecture Notes in Computer Science(), vol 12977. Springer, Cham. https://doi.org/10.1007/978-3-030-86523-8_40

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  • DOI: https://doi.org/10.1007/978-3-030-86523-8_40

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  • Print ISBN: 978-3-030-86522-1

  • Online ISBN: 978-3-030-86523-8

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