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

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.