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Document Summarization Using Sentence Features

Author

Listed:
  • Rasmita Rautray

    (Department of Computer Science and Engineering, Institute of Technical Education & Research, Siksha ‘O' Anusandhan University, Bhubaneswar, India)

  • Rakesh Chandra Balabantaray

    (Department of Computer Science and Engineering, International Institute of Information Technology, Bhubaneswar, India)

  • Anisha Bhardwaj

    (Department of Computer Science and Engineering, Institute of Technical Education & Research, Siksha ‘O' Anusandhan University, Bhubaneswar, India)

Abstract

Problem of exponential growth of information available electronically, there is an increasing demand for text summarization. Text summarization is the process of extracting the contents of the original text in a shorter form that provides useful information to the user. This paper presents a summarizer to produce summaries while reducing the redundant information and maximizing the summary relevancy. The proposed model takes several features into an account, including title feature, sentence weight, term weight, sentence position, inter sentence similarity, proper noun, thematic word and numerical data. The score of each feature for the model can be obtained from the document sets. However, the results of such models are evaluated to measure their performance based on F-score of extracted sentences at 20% compression rate on a C-50 data corpus. Experimental studies on C-50 data corpus, PSO summarizer show significantly better performance compared to other summarizer.

Suggested Citation

  • Rasmita Rautray & Rakesh Chandra Balabantaray & Anisha Bhardwaj, 2015. "Document Summarization Using Sentence Features," International Journal of Information Retrieval Research (IJIRR), IGI Global, vol. 5(1), pages 36-47, January.
    • Handle: RePEc:igg:jirr00:v:5:y:2015:i:1:p:36-47
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    Cited by:

    1. Rautray, Rasmita & Balabantaray, Rakesh Chandra, 2017. "Cat swarm optimization based evolutionary framework for multi document summarization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 477(C), pages 174-186.

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