Research Article | Open Access
SUMMARY GENERATION WEBAPP USING NLP
Mrs.P.ARCHANA, KALAKONDA VIKAS KUMAR, KADAVOINA SRIJA, BOLLAM RAHUL, GARLAPATI YASHAS
Text summarization is the process of generating short, fluent, and most importantly accurate summary
of a respectively longer text document. The main idea behind automatic text summarization is to be
able to find a short subset of the most essential information from the entire set and present it in a
human readable format. As online textual data grows, automatic text summarization methods have the
potential to be very helpful because more useful information can be read in a short time. For the front
end we use html, css and for backend flask. The webapp is built using Flaskandthe modelthat
isrunning inthe backendofthiswebapp isbuilt usingspacy library. The extractive technique scans the
original d ocument to find the relevant sentences and extracts only that information from it. The
abstractive summarization technique interprets the original text before generating the summary. Using
state of the art NLP techniques, specifically SBERT and SpaCy, our webapplication allows users to
effortlessly generate concise and coherent summaries from extensive textual content. Whether it's
news articles, research papers, or any textual data, our tool empowers users to quickly grasp the key
insights without the need to read through entire documents. The coretechnology behind our web app
is SBERT, a novelsentence embedding model. SBERT captures the semantic relationships between
sentences, enabling the generation of informative and context aware summaries. Additionall y, SpaCy,
a leading NLP library, aids in linguistic analysis and further improves the qualityof the generated
Keywords: Summary Generation, Natural Language Processing (NLP), SBERT, Spacy, Text Summarization, Web Application, Information Overloa d, Accuracy, Semantic Analysis.
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