TEXT-SUMMARIZER and Text-Summarization-and-information-extraction

TEXT-SUMMARIZER
50
Established
Text-Summarization-and-information-extraction
21
Experimental
Maintenance 10/25
Adoption 7/25
Maturity 16/25
Community 17/25
Maintenance 0/25
Adoption 1/25
Maturity 8/25
Community 12/25
Stars: 39
Forks: 11
Downloads: —
Commits (30d): 0
Language: HTML
License: MIT
Stars: 1
Forks: 1
Downloads: —
Commits (30d): 0
Language: Python
License: —
No Package No Dependents
No License Stale 6m No Package No Dependents

About TEXT-SUMMARIZER

Amey-Thakur/TEXT-SUMMARIZER

Machine Learning Project to Compare and Evaluate Text Summarization Algorithms Using SpaCy, NLTK, Gensim, and Sumy.

This tool helps busy professionals quickly grasp the core ideas from lengthy texts or web articles. You input the original text or a URL, and it provides several concise summaries, comparing how different algorithms condense the information. It's ideal for anyone who needs to extract key insights without reading through entire documents.

information-retrieval content-curation research-analysis reading-efficiency knowledge-management

About Text-Summarization-and-information-extraction

Ekanth-Sai/Text-Summarization-and-information-extraction

This project focuses on developing advanced algorithms for automatic text summarization and information extraction. Leveraging natural language processing techniques, the system will efficiently condense lengthy documents into concise summaries, while simultaneously identifying and extracting key information.

Scores updated daily from GitHub, PyPI, and npm data. How scores work