{"id":24539655,"url":"https://github.com/codeasarjun/textsummary","last_synced_at":"2025-07-25T16:34:06.139Z","repository":{"id":224190364,"uuid":"762670268","full_name":"codeasarjun/Textsummary","owner":"codeasarjun","description":"A simple text summarizer built with the Flask web framework. It uses TF-IDF (Term Frequency-Inverse Document Frequency) to summarize text. 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The summarization process is based on TF-IDF (Term Frequency-Inverse Document Frequency). Users can input text through a web interface, and the application generates a summary based on the provided input.\n\n\nProject Structure\n•\tapp.py: The main file that contains the Flask application.\n•\tCORE/text_summarizer.py: The module with the function for text summarization.\n•\ttemplates/index.html: The HTML template for the web interface.\n\n\nDependencies\n\n\n•\tFlask: A micro web framework for Python.\n\n\n•\tNLTK: A natural language processing library.\n\n\n•\tscikit-learn: A machine learning library for Python.\n\n\nFeatures 🚀\n\n\nTF-IDF Calculation 🔍\u003cbr\u003e\nThe core functionality of the project revolves around calculating TF-IDF scores for words in the input text. TF-IDF scores help identify the significance of each word in the context of the entire document corpus.\n\nSentence Extraction 📄\nUsing TF-IDF scores, the system extracts the most important sentences from the original text. These sentences encapsulate the essential information and contribute to the final summary.\nInstallation\n1.\tInstall the required Python packages:\nbashCopy code\npip install Flask nltk scikit-learn \n2.\tDownload NLTK data:\npythonCopy code\nimport nltk nltk.download('punkt') nltk.download('stopwords') \n3.\tRun the Flask application:\nbashCopy code\npython app.py \n4.\tAccess the web interface at http://localhost:5000 in your browser.\nUsage\n1.\tEnter the text you want to summarize in the provided textarea.\n2.\tOptionally, specify the number of sentences for the summary.\n3.\tClick the \"Summarize\" button.\n4.\tView the original text and the generated summary.\n\n   \n## Notes\u003cbr\u003e\n•\tThis project uses a TF-IDF-based extractive summarization approach.\u003cbr\u003e\n•\tThe summarization function is located in the CORE/text_summarizer.py file.\u003cbr\u003e\n•\tAdjust the parameters in the tfidf_summarize function for desired summarization length.\u003cbr\u003e\n## Future Improvements\n•\tExplore more advanced summarization methods, such as BERT-based models.\u003cbr\u003e\n•\tEnhance the user interface with additional features.\u003cbr\u003e\n\n## Output \u003chr\u003e\n\u003cimg src=\"https://github.com/codeasarjun/Textsummary/blob/main/img/output.png\"\u003e\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcodeasarjun%2Ftextsummary","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcodeasarjun%2Ftextsummary","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcodeasarjun%2Ftextsummary/lists"}