Single Document Extractive Text Summarization Using Neural Networks and Genetic Algorithm

  • Niladri ChatterjeeEmail author
  • Gautam Jain
  • Gurkirat Singh Bajwa
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 858)


The presented paper proposes an extractive text summarization technique for single documents using Neural Networks and Genetic Algorithms. The Neural Network helps to define a fitness function to express mathematically the quality of the generated summary through six desired properties which are theme similarity, cohesion, sentiment, readability, aggregate similarity and sentence position. Genetic Algorithm maximizes the above-mentioned fitness function, and extracts the most important sentences to create the extractive summary. The results are compiled using DUC2002 data as a benchmark and calculated using the precision-recall technique. They are compared with techniques using Genetic Algorithm, Neural Network and a summarizer made by Microsoft. The comparison between the results clearly demonstrates the superiority of the technique and is very encouraging for future work in this area.


Text summarization Neural networks Genetic algorithms Sentence extraction 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Niladri Chatterjee
    • 1
    Email author
  • Gautam Jain
    • 1
  • Gurkirat Singh Bajwa
    • 1
  1. 1.Department of MathematicsIndian Institute of TechnologyNew DelhiIndia

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