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Machine Learning-Powered Cloud-Based Text Summarization

  • Conference paper
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Evolutionary Artificial Intelligence (ICEASSM 2017)

Part of the book series: Algorithms for Intelligent Systems ((AIS))

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Abstract

The goal of text summarization, a crucial task in natural language processing (NLP), is to reduce enormous amounts of text into brief summaries while retaining the most important details. The development of cloud computing and improvements in machine learning have opened new possibilities for improving the effectiveness and precision of text summarization systems. This study thoroughly analyzes text summarizing methods that use cloud computing and machine learning techniques. The system programmer goes through the advantages of using cloud-based resources, including scalability, usability, and affordability, to manage the computationally demanding nature of text summarization. Additionally, the system programmer investigates alternative machine learning strategies, such as abstractive and extractive summarization, and the system emphasize their advantages and disadvantages and how to strengthen text summarization applications by integrating cloud services like cloud-based machine learning platforms and natural language processing APIs. The developer looks at case studies and practical examples that show how cloud computing and machine learning may be used to effectively conduct text summarization. The system highlights the significance of ongoing innovation and cooperation to fully realize the potential of text summarization approaches in the age of cloud computing and machine learning. The system also addresses future research paths and potential problems in this field.

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Author information

Authors and Affiliations

  1. Parachute Health, Princeton, NJ, USA

    Mayur Rele

  2. University of Cumberlands, Williamsburg, KY, USA

    Dipti Patil

Authors
  1. Mayur Rele
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  2. Dipti Patil
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Corresponding author

Correspondence to Mayur Rele .

Editor information

Editors and Affiliations

  1. Faculty of Innovation and Technology, Taylor’s University, Subang Jaya, Selangor, Malaysia

    David Asirvatham

  2. University of Southeast Norway, Notodden, Norway

    Francisco M. Gonzalez-Longatt

  3. Gdansk University of Technology, Gdańsk, Poland

    Przemyslaw Falkowski-Gilski

  4. Professor of Computer Engineering, Papua New Guinea University of Technology, Lae, Papua New Guinea

    R. Kanthavel

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Rele, M., Patil, D. (2024). Machine Learning-Powered Cloud-Based Text Summarization. In: Asirvatham, D., Gonzalez-Longatt, F.M., Falkowski-Gilski, P., Kanthavel, R. (eds) Evolutionary Artificial Intelligence. ICEASSM 2017. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-8438-1_4

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