International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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An Integrated Framework for Extractive and Abstractive Summarization of Web and User-Provided Text

Author(s) Ms. Sukrutha K S, Ms. Rajitha V, Ms. Abhilasha H S, Ms. Aishwarya N, Ms. Anusha C, Ms. Sinchana H M
Country India
Abstract The rapid growth of online textual content—particularly in the form of news articles and web documents—has heightened the demand for advanced summarization tools. This study introduces a comprehensive framework that combines extractive and abstractive summarization methods to accommodate diverse input types, such as direct web links and manually entered text. The system can generate clear, concise summaries either by selecting key sentences or by creating paraphrased versions of the content, depending on the user's choice. A user-friendly web interface enables smooth interaction with both summarization approaches. To evaluate the system’s effectiveness, we use established metrics like ROUGE and BLEU, which assess summary quality in terms of relevance and fluency. Results show that the model reliably produces summaries that closely match reference materials. This work advances automated summarization by offering a flexible, real-time tool for digesting information from varied sources efficiently.
Keywords Text Summarization, Extractive Summarization, Abstractive Summarization, Web Article Summarization, Manual Text Input, BERT, GPT, T5, ROUGE Score, BLEU Score
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 3, May-June 2025
Published On 2025-06-11
DOI https://doi.org/10.36948/ijfmr.2025.v07i03.47684
Short DOI https://doi.org/g9qp34
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E-ISSN 2582-2160
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