Paper Title
Kannada Keyword-Based Text Summarization
Article Identifiers
Authors
Shwetha Kamath , Tanvi Chandra , Swathi P Hegde , Sindhu D V , Sneha Devidas Gunagi
Keywords
Automatic, Extractive, Term Frequency, GSS, Text ranking
Abstract
The practice of condensing text material into a summary while retaining the major ideas is known as summarization. To best communicate the message concealed in the text, extractive summarizers operate with the provided text to extract sentences. The idea of discovering keywords and extracting sentences with more keywords than the rest is at the heart of most extractive summarization techniques. With an emphasis on essential terms, relevant words with a higher frequency than others are typically extracted to create keywords. We employed the TF (Term Frequency) model and GSS coefficients in the suggested system to extract keywords for the text ranking process. In this study, we presented a method for automatically extracting keywords from Kannada datasets for text summarizing.
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How To Cite (APA)
Shwetha Kamath, Tanvi Chandra, Swathi P Hegde, Sindhu D V, & Sneha Devidas Gunagi (April-2023). Kannada Keyword-Based Text Summarization. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(4), f555-f557. https://ijnrd.org/papers/IJNRD2304564.pdf
Issue
Volume 8 Issue 4, April-2023
Pages : f555-f557
Other Publication Details
Paper Reg. ID: IJNRD_192487
Published Paper Id: IJNRD2304564
Downloads: 000121988
Research Area: Engineering
Country: Mangalore, Karnataka, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2304564.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2304564
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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)
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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition
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