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Summarizing News Articles Using Question-and-Answer Pairs via Learning

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The Semantic Web – ISWC 2019 (ISWC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11778))

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Abstract

The launch of the new Google News in 2018 (https://www.blog.google/products/news/new-google-news-ai-meets-human-intelligence/.) introduced the Frequently asked questions feature to structurally summarize the news story in its full coverage page. While news summarization has been a research topic for decades, this new feature is poised to usher in a new line of news summarization techniques. There are two fundamental approaches: mining the questions from data associated with the news story and learning the questions from the content of the story directly. This paper provides the first study, to the best of our knowledge, of a learning based approach to generate a structured summary of news articles with question and answer pairs to capture salient and interesting aspects of the news story. Specifically, this learning-based approach reads a news article, predicts its attention map (i.e., important snippets in the article), and generates multiple natural language questions corresponding to each snippet. Furthermore, we describe a mining-based approach as the mechanism to generate weak supervision data for training the learning based approach. We evaluate our approach on the existing SQuAD dataset (https://rajpurkar.github.io/SQuAD-explorer/.) and a large dataset with 91K news articles we constructed. We show that our proposed system can achieve an AUC of 0.734 for document attention map prediction, a BLEU-4 score of 12.46 for natural question generation and a BLEU-4 score of 24.4 for question summarization, beating state-of-art baselines.

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Notes

  1. 1.

    Private communication with Google’s news team: FAQ is shown to improve users’ understanding of the news stories in user studies, which is an important launch criteria.

  2. 2.

    Private communication.

  3. 3.

    https://rajpurkar.github.io/SQuAD-explorer/.

  4. 4.

    https://nlp.stanford.edu/software/tagger.html.

  5. 5.

    Note there can be multiple questions with the same answer snippet, for example, another question candidate could be: Under which name is the Black Eagle Brewery also known? Our learning based approach can learn those diverse questions provided that the training data captures the same diversity.

  6. 6.

    https://nlp.stanford.edu/software/tokenizer.shtml.

  7. 7.

    https://nlp.stanford.edu/software/tokenizer.shtml.

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Author information

Authors and Affiliations

  1. Google Research, New York, USA

    Xuezhi Wang & Cong Yu

Authors
  1. Xuezhi Wang
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  2. Cong Yu
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Corresponding author

Correspondence to Xuezhi Wang .

Editor information

Editors and Affiliations

  1. Fondazione Bruno Kessler, Trento, Italy

    Chiara Ghidini

  2. Linköping University, Linköping, Sweden

    Olaf Hartig

  3. University of Bonn, Bonn, Germany

    Maria Maleshkova

  4. University of Economics Prague, Prague, Czech Republic

    Vojtěch Svátek

  5. University of Illinois at Chicago, Chicago, IL, USA

    Isabel Cruz

  6. University of Chile, Santiago, Chile

    Aidan Hogan

  7. Memect Technology, Beijing, China

    Jie Song

  8. Mines Saint-Etienne, Saint-Etienne, France

    Maxime Lefrançois

  9. Inria Sophia Antipolis - Méditerranée, Sophia Antipolis, France

    Fabien Gandon

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Wang, X., Yu, C. (2019). Summarizing News Articles Using Question-and-Answer Pairs via Learning. In: Ghidini, C., et al. The Semantic Web – ISWC 2019. ISWC 2019. Lecture Notes in Computer Science(), vol 11778. Springer, Cham. https://doi.org/10.1007/978-3-030-30793-6_40

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  • DOI: https://doi.org/10.1007/978-3-030-30793-6_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30792-9

  • Online ISBN: 978-3-030-30793-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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