This project seeks to create a comprehensive system for summarising research papers by harnessing the latest advancements in AI and NLP. By merging abstractive text summarization with LLMs and the RAG methodology, we anticipate developing a unique and effective approach to extracting valuable insights from research papers

In this project, we try to investigate and learn both theoretical foundations and practical approaches to solve the abstractive multi-document text summarization problem in natural language processing(NLP)

An advanced and interactive text summarization application built using Hugging Face’s `BART` transformer. This project showcases the power of abstractive summarization using state-of-the-art NLP models and offers both a Gradio web interface and command-line usage.

Enormous number of video recordings are being created and shared on the Internet through out the day. It has become really difficult to spend time in watching such videos which may have a longer duration than expected and sometimes our efforts may become futile if we couldn't find relevant information out of it. Summarizing transcripts of such videos automatically allows us to quickly look out for the important patterns in the video and helps us to save time and efforts to go through the whole content of the video. This project will give us an opportunity to have hands on experience with state of the art NLP technique for abstractive text summarization and implement an interesting idea suitable for intermediates and a refreshing hobby project for professionals.

NLP based project on extractive and abstractive text summarization

This Text Summarization Project aims to develop a tool that efficiently condenses long articles, papers, or documents into concise summaries while preserving the key information and context. Utilizing advanced Natural Language Processing (NLP) techniques, this project focuses on both extractive and abstractive summarization methods.

This Repository holds the code and PPT for the NLP Project that focuses on Abstractive Text Summarization

An end-to-end NLP project demonstrating extractive and abstractive text summarization with GPU acceleration and interactive UI.

An NLP-based Python project that automatically summarizes long text into concise summaries using extractive and abstractive methods.

📝 End-to-End NLP Text Summarization An end-to-end Natural Language Processing project that builds, trains, and deploys a text summarization system. It covers the full pipeline: data preprocessing → feature engineering → model training (Seq2Seq/Transformer) → evaluation → deployment. 🔹 Highlights Abstractive & extractive summarization approache

This project implements text summarization using NLP techniques, supporting extractive & abstractive methods, TF-IDF, POS tagging, and chunking. It includes a Streamlit UI for interaction and can be deployed via Google Colab using ngrok.

A smart, end-to-end article summarization pipeline using BERT and T5. This project combines extractive and abstractive NLP techniques to generate concise, high-quality summaries from long-form text, with a focus on real-world usability, evaluation, and insight generation.

Available now on Apple App Store. I made this as a project for my Machine Learning Fundamentals class. I was fascinated by NLP and knew I wanted to build something that incorporated NLP. This is a text summarizer app that utilizes the Apple Vision framework to identify text using your camera. The text summarization utilizes the Reductio library by fdzsergio. Reductio uses the TextRank algorithm to provide an extractive summary of the text. There are 2 types of summaries currently in NLP: extractive and abstractive. An extractive summary is an NLP concept that means the text being summarized uses sentences from the text to make up the summary. Extractive summaries are still in their infancy and may not give you the best results every time; they aren't perfect. An abstractive summary attempts to create its own sentences from the text.

Abstractive Prose Summary of Tweets

Project for NLP Abstractive Text Summarization using BART Model

An NLP project for abstractive and extractive text summarization using transformer models.

A natural language processing (NLP) project for abstractive and extractive text summarization.

An NLP project for abstractive and extractive text summarization using advanced transformer models.

An end-to-end project for abstractive and extractive text summarization using advanced NLP models.

An advanced NLP project focusing on abstractive and extractive text summarization techniques using transformer models.

An advanced NLP project for abstractive and extractive text summarization using transformer models and deep learning.

An NLP project focused on abstractive and extractive text summarization techniques using state-of-the-art models.

The Text Summarizer is a Python-based natural language processing (NLP) project that leverages the Hugging Face Transformers library to perform abstractive text summarization.

This project focuses on implementing abstractive text summarization for the Telugu language using Natural Language Processing (NLP). Abstractive summarization involves taking long Telugu text inputs and generating concise summaries in a few lines.

A Python-based NLP project for abstractive and extractive text summarization using state-of-the-art models and techniques.

A Streamlit-based NLP project for extractive and abstractive text summarization using Hugging Face Transformers, PyTorch, and a modular pipeline.

End-to-end NLP project for automatic text summarization using extractive and abstractive methods, covering data processing, model development, and deployment.

A Hugging Face-powered NLP project that implements transformer-based abstractive text summarization, demonstrating real-world applications of Large Language Models (LLMs) in content compression, document summarization, and AI-driven text understanding.

Text Summarization is a Natural Language Processing (NLP) project that aims to generate concise summaries from long pieces of text using modern deep learning models. This project demonstrates both extractive and abstractive summarization approaches implemented using Python and various NLP libraries.

🚀 Abstractive Text Summarization using Transformer-based NLP models. This project leverages deep learning and attention mechanisms to generate human-like summaries from long text inputs