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A Comprehensive Survey on Topic Modeling in Text Summarization

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Micro-Electronics and Telecommunication Engineering (ICMETE 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 373))

Abstract

Topic modeling is the statistical model for discovering hidden topics or keywords in a collection of documents. Topic modeling is also considered a probabilistic model for learning, analyzing, and discovering topics from the document collection. The most popular techniques for topic modeling are latent semantic analysis (LSA), probabilistic latent semantic analysis (pLSA), latent Dirichlet allocation (LDA), and the recent deep learning-based lda2vec. LDA is most commonly used in extractive multi-document summarization to determine whether the extracted sentence reflects the concept of the input document. In this paper, we will try to explore various multi-document summarization techniques that use LDA as a topic modeling method for improving final summary coverage and to reduce redundancy. Finally, we compared LDA and LSA using the Genism toolkit, and our experiment results show that LDA outperforms LSA if we increase the number of features considered for sentence selection.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Chennai, India

    G. Bharathi Mohan & R. Prasanna Kumar

Authors
  1. G. Bharathi Mohan
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  2. R. Prasanna Kumar
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Corresponding author

Correspondence to G. Bharathi Mohan .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ghaziabad, India

    Devendra Kumar Sharma

  2. National Taipei University of Business, Taipei, Taiwan

    Sheng-Lung Peng

  3. Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ghaziabad, India

    Rohit Sharma

  4. Odessa State Environmental University, Odessa, Ukraine

    Dmitry A. Zaitsev

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Mohan, G.B., Kumar, R.P. (2022). A Comprehensive Survey on Topic Modeling in Text Summarization. In: Sharma, D.K., Peng, SL., Sharma, R., Zaitsev, D.A. (eds) Micro-Electronics and Telecommunication Engineering . ICMETE 2021. Lecture Notes in Networks and Systems, vol 373. Springer, Singapore. https://doi.org/10.1007/978-981-16-8721-1_22

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  • DOI: https://doi.org/10.1007/978-981-16-8721-1_22

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-8720-4

  • Online ISBN: 978-981-16-8721-1

  • eBook Packages: EngineeringEngineering (R0)

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