Developing Gujarati Article Summarization Utilizing Improved Page-Rank System

Riddhi Kevat and Sheshang Degadwala

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024, vol. 10, issue 2, 293-299

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.

Keywords: Gujarati; Text Summarization; Pagerank Algorithm; Comparative Study; Frequency-Based Summarization; TFIDF; LexRank (search for similar items in EconPapers)
Date: 2024
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT2410222
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v10:y2024:i2:id:50

DOI: 10.32628/CSEIT2410222

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