A fully offline document summarizer built in pure Python. Uses TF-IDF scoring, position weighting...A fully offline document summarizer built in pure Python. Uses TF-IDF scoring, position weighting...
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Umaima Iqbal
• 17h
A fully offline document summarizer built in pure Python. Uses TF-IDF scoring, position weighting, and Jaccard deduplication to extract the most important sentences from any PDF, DOCX, or TXT file — each labeled with a relevance percentage.
The result looks like this: [1] [100% relevance] The algorithm achieved 94% accuracy on benchmark tests. [2] [81% relevance] Training was performed on 50,000 labeled samples. [3] [67% relevance] Results were validated using 5-fold cross validation.
Supports PDF, Word, and TXT files. Saves summaries to your computer. Runs completely offline. No subscriptions, no API keys, no internet required.
AI AutomationVibe CodingDesktop Apps DevelopmentGitHubTkinterPython
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