In this project, we propose to implement a web application that can summarize a text or a Wikipedia link. We have additionally been given an opportunity to compare different methods of summarization.
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
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.
Summarises text for articles in Hindi and compares a custom TF-IDF algorithm with the baseline
The NLP Text Summarizer uses Natural Language Processing techniques to generate concise summaries of text documents, supporting both extractive and abstractive methods.
A simple implementation and UI of Summary Transformer models. Specifically The pszemraj/led-large-book-summary
An application to get text summary of a large article or text using NLP
Text summarization based on SVD & NMF
Simple text summariser using NLTK in python
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