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A Comparative Study of Classifiers for Extractive Text Summarization

  • Anshuman PattanaikEmail author
  • Sanjeevani Subhadra Mishra
  • Madhabananda Das
Conference paper
  • 18 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1101)

Abstract

Automatic text summarization (ATS) is a widely used approach. Through the years, various techniques have been implemented to produce the summary. An extractive summary is a traditional mechanism for information extraction, where important sentences are selected which refers to the basic concepts of the article. In this paper, extractive summarization has been considered as a classification problem. Machine learning techniques have been implemented for classification problems in various domains. To solve the summarization problem in this paper, machine learning is taken into consideration, and KNN, random forest, support vector machine, multilayer perceptron, decision tree and logistic regression algorithm have been implemented on Newsroom dataset.

Keywords

Text summarization Extractive Sentence scoring Machine learning 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Anshuman Pattanaik
    • 1
    Email author
  • Sanjeevani Subhadra Mishra
    • 1
  • Madhabananda Das
    • 1
  1. 1.School of Computer EngineeringKalinga Institute of Industrial Technology (Deemed-to-be University)BhubaneswarIndia

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