A robust web application leveraging multiple NLP algorithms (SpaCy, NLTK, Gensim, Sumy) to summarize textual content and URL sources, featuring a comparative analysis interface for evaluating summarization quality.
Generates summaries from texts - Wikipedia, Textbox and PDF.
Shortens the paragraph depending on the given keywords using Levenshtein distance
Python package for extractive text summarization using centroid distance
Summarises text for articles in Hindi and compares a custom TF-IDF algorithm with the baseline
This is a full-featured summarization pipeline that processes "ted_talks" transcripts or any of your choice into concise summaries using state-of-the-art NLP models. The app is designed with a modular architecture and supports multiple interfaces, including a UI, API, and CLI, for maximum flexibility and usability.
A simple implementation and UI of Summary Transformer models. Specifically The pszemraj/led-large-book-summary
Text summarization based on SVD & NMF
The NLP Text Summarizer uses Natural Language Processing techniques to generate concise summaries of text documents, supporting both extractive and abstractive methods.
An application to get text summary of a large article or text using NLP
Simple text summariser using NLTK in python
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