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Extractive Text Summarization on Large-scale Dataset Using K-Means Clustering

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Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence (IEA/AIE 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13343))

Abstract

Extractive text summarization is one of the most important tasks in natural language processing. In this work, we use K-Means clustering to create the clusters on the Vietnamese large-scale dataset, then use these clusters to extract the most relevant sentences on the single-document to produce the summary. At first, we collected the articles in the Vietnamese online newspapers, cleaned up and packaged them into the dataset, after that we applied our summarization model for the experimentation. The best F-Score of this model based on ROUGE-2 and ROUGE-L are 15.48% and 28.68%.

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Notes

  1. 1.

    This dataset is popular used in text summary research.

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Author information

Authors and Affiliations

  1. Can-Tho University, Can-Tho, Vietnam

    Ti-Hon Nguyen & Thanh-Nghi Do

Authors
  1. Ti-Hon Nguyen
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  2. Thanh-Nghi Do
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Corresponding author

Correspondence to Ti-Hon Nguyen .

Editor information

Editors and Affiliations

  1. i-SOMET, Inc., Morioka-shi, Iwate, Japan

    Hamido Fujita

  2. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China

    Philippe Fournier-Viger

  3. Texas State University, San Marcos, TX, USA

    Moonis Ali

  4. Shanghai University of Finance and Economics, Shanghai, China

    Yinglin Wang

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Cite this paper

Nguyen, TH., Do, TN. (2022). Extractive Text Summarization on Large-scale Dataset Using K-Means Clustering. In: Fujita, H., Fournier-Viger, P., Ali, M., Wang, Y. (eds) Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence. IEA/AIE 2022. Lecture Notes in Computer Science(), vol 13343. Springer, Cham. https://doi.org/10.1007/978-3-031-08530-7_62

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  • DOI: https://doi.org/10.1007/978-3-031-08530-7_62

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-08529-1

  • Online ISBN: 978-3-031-08530-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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