Used Python “keras” package to develop a unidirectional LSTM based deep learning model “encoder-decoder networks” for title generation on news data set. Improved the architecture by using attention. Created tensorboard visualizations to show the validation accuracy and loss. Evaluated the summaries using the ROUGE score.

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 project examining BERT-based abstractive summarization model for gender bias using a lightweight word choice based approach.

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.

📝 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 powerful tool to generate concise summaries from Wikipedia articles using both extractive and abstractive summarization techniques. The project integrates NLP models like BART and PEGASUS to provide quick, readable content for users seeking fast insights.

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.

This project uses NLP models to generate concise summaries of educational content, focusing on abstractive summarization. The summaries are then converted into Grade 2 Braille using Unicode encoding, allowing visually impaired users to access key information in a Braille-readable format.

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.

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Abstractive Prose Summary of Tweets

Abstractive tweet summarization for NLP Course Project

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.

This is a python project based on NLP which contains extractive and abstractive summarization

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

This is a NLP project for abstractive news summarization. Using hugging face Transformer and pyTorch libraries.

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

"An end-to-end NLP project fine-tuning PEGASUS for abstractive summarization of English news articles."

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

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.