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Developing Gujarati Article Summarization Utilizing Improved Page-Rank System

Author

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  • Riddhi Kevat
  • Sheshang Degadwala

Abstract

This research delves deep into the domain of Gujarati text summarization, where we employ an improved version of the PageRank algorithm to enhance both efficiency and accuracy. The study is meticulously structured around a comprehensive comparative analysis, juxtaposing our innovative approach against well-established methods like frequency-based summarization, TF-IDF, and LexRank. Through our rigorous investigation, we unveil compelling findings that showcase the superior performance of the enhanced PageRank algorithm, delivering summaries that are not only more concise but also contextually relevant, thus retaining the inherent linguistic intricacies characteristic of Gujarati. This exploration signifies a significant leap forward in the realm of text summarization techniques for Gujarati, carrying broad implications for bolstering information retrieval capabilities and advancing natural language processing functionalities within this linguistic domain.

Suggested Citation

  • Riddhi Kevat & Sheshang Degadwala, 2024. "Developing Gujarati Article Summarization Utilizing Improved Page-Rank System," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 10(2), pages 293-299, April.
    • Handle: RePEc:jbh:ijsrcs:v10:y2024:i2:id:50
    DOI: 10.32628/CSEIT2410222
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT2410222
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