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We all use software that employs text summarising. The platform that releases articles covering daily news, entertainment, and sports is the focal point of numerous applications. Owing to our hectic routines, we frequently opt to peruse a summary of an article before delving into its entirety.Reading a summary enables us to determine our areas of interest and provides a concise overview of the plot. A summary is described as a text created from one or more texts that delivers key information from the original text(s), is no longer than half the original text(s), and is typically much less than that. Using a natural language processing technique called text summarization, users can condense a huge volume of text into a little amount of text. The challenge is to maintain the main ideas and overall meaning in a succinct and fluid explanation. The general-purpose graph-based ranking algorithm TextRank is used in NLP. Natural Language Processing uses the text summarization method known as TextRank to create document summaries.
Keywords:
Text Rank, NLTK ,Abstractive summarization, Extractive summarization, Lexical Analysis, TF-IDF.
Cite Article:
"Text Summarization Using Text Rank Algorithm", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 7, page no.323 - 334, July-2023, Available :http://www.ijrti.org/papers/IJRTI2307049.pdf
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000205170
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator