International Research Journal of Education and Technology

With the increasing volume of academic research, efficiently extracting key insights from lengthy articles has become a necessity for researchers and professionals. This paper introduces an application for summarizing research papers using Natural Language Processing (NLP) techniques. The application employs abstractive summarization powered by the Pegasus model, integrated with React.js for a user-friendly interface and Flask as the backend framework. Features include PDF uploading, customizable summary lengths, and rapid summary generation. Performance metrics and user feedback highlight the tool's ability to deliver concise, coherent, and contextually accurate summaries, enhancing research productivity.

Key Words: Abstractive Summarization, Research Paper Summarization, Natural Language Processing, Pegasus Model, React.js, Flask, NLTK, SpaCy.