https://github.com/bdurga26/text-summarizer

https://github.com/bdurga26/text-summarizer

Last synced: 5 days ago
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• Host: GitHub
• URL: https://github.com/bdurga26/text-summarizer
• Owner: BDurga26
• Created: 2024-09-15T15:48:36.000Z (2 months ago)
• Default Branch: master
• Last Pushed: 2024-09-15T15:51:14.000Z (2 months ago)
• Last Synced: 2024-09-15T17:08:33.749Z (2 months ago)
• Language: Jupyter Notebook
• Size: 2.93 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
A text summarizer is a tool or algorithm that condenses a large body of text into a shorter version while retaining the most important information. 


It extracts key ideas, concepts, or sentences from the original content, reducing its length without losing its essential meaning.


How the Python Extractive Summarizer Works: 


Tokenization: The input text is broken down into individual sentences and words. This allows the algorithm to process the text on a granular level. 


Stopword Removal: Common, less meaningful words (like "the", "and", etc.) are removed to focus on the more important terms. 


Word Frequency Calculation: The importance of words is determined by their frequency in the text. Words that appear more frequently (excluding stopwords) are considered more significant.


Sentence Scoring: Each sentence is scored based on the importance of the words it contains. Sentences with more high-frequency words are given higher scores.


Summary Generation: Sentences with the highest scores are selected to form the summary. You can control the length of the summary by adjusting the percentage of the text to retain (e.g., 30% of the original text).