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CLTS+: A New Chinese Long Text Summarization Dataset with Abstractive Summaries

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Artificial Neural Networks and Machine Learning – ICANN 2022 (ICANN 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13529))

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Abstract

The abstractive methods lack of creative ability is particularly a problem in automatic text summarization. The summaries generated by models are mostly extracted from the source articles. One of the main causes for this problem is the lack of dataset with abstractiveness, especially for Chinese. In order to solve this problem, we paraphrase the reference summaries in CLTS, the Chinese Long Text Summarization dataset, correct errors of factual inconsistencies, and propose the first Chinese Long Text Summarization dataset with a high level of abstractiveness, CLTS+, which contains more than 180K article-summary pairs and is available online. Additionally, we introduce an intrinsic metric based on co-occurrence words to evaluate the dataset we constructed. We analyze the extraction strategies used in CLTS+ summaries against other datasets to quantify the abstractiveness and difficulty of our new data and train several baselines on CLTS+ to verify the utility of it for improving the creative ability of models.

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Notes

  1. 1.

    https://github.com/lxj5957/CLTS-plus-Dataset.

  2. 2.

    https://www.google.cn/intl/zh_cn/sheets/about/.

  3. 3.

    https://github.com/fxsjy/jieba.

  4. 4.

    https://www.chinanews.com/.

  5. 5.

    https://news.qq.com/.

  6. 6.

    http://news.sohu.com/.

  7. 7.

    http://www.bj.chinanews.com/news/2020/1020/79344.html.

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Authors and Affiliations

  1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Xiaojun Liu, Shunan Zang, Chuang Zhang, Xiaojun Chen & Yangyang Ding

  2. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

    Xiaojun Liu & Shunan Zang

Authors
  1. Xiaojun Liu
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  2. Shunan Zang
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  3. Chuang Zhang
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  4. Xiaojun Chen
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  5. Yangyang Ding
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Corresponding author

Correspondence to Chuang Zhang .

Editor information

Editors and Affiliations

  1. University of the West of England, Bristol, UK

    Elias Pimenidis

  2. Lancaster University, Lancaster, UK

    Plamen Angelov

  3. Digital Innovation, Teesside University, Middlesbrough, UK

    Chrisina Jayne

  4. Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

  5. The University of the West of England, Bristol, UK

    Mehmet Aydin

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Liu, X., Zang, S., Zhang, C., Chen, X., Ding, Y. (2022). CLTS+: A New Chinese Long Text Summarization Dataset with Abstractive Summaries. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13529. Springer, Cham. https://doi.org/10.1007/978-3-031-15919-0_7

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  • DOI: https://doi.org/10.1007/978-3-031-15919-0_7

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