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Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM29913
Volume:
8 |
4 |
Apr 2024
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
In the era of exponential growth in online data, the proliferation of online information necessitates effective strategies for text summarization to distill vast amounts of data into concise, digestible insights. This paper presents a comprehensive investigation into text summarization methodologies within the context of a Text Intelligence Engine project, with a primary emphasis on the Text Rank algorithm. Leveraging the principles of graph-based ranking, our approach harnesses the power of Text Rank to identify key concepts and generate coherent summaries. Through meticulous experimentation and rigorous evaluation, we demonstrate the robustness and effectiveness of Text Rank in extracting salient information from diverse textual sources. Furthermore, we explore the integration of Text Rank with machine learning techniques to enhance summarization performance. By elucidating the theoretical foundations and practical applications of Text Rank, this paper contributes to the advancement of text intelligence systems, offering valuable insights and guidelines for researchers and practitioners in the field.
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Publication Date
12/31/45382
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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