Popular GitHub Repositories for Pegasus

Open Source Pre-training Model Framework in PyTorch & Pre-trained Model Zoo

Official implementation of the paper "IteraTeR: Understanding Iterative Revision from Human-Written Text" (ACL 2022)

Abstractive text summarization by fine-tuning seq2seq models.

Codes for our paper "CTRLEval: An Unsupervised Reference-Free Metric for Evaluating Controlled Text Generation" (ACL 2022)

[EMNLP 2023] FaMeSumm: Investigating and Improving Faithfulness of Medical Summarization. Support BART, PEGASUS, T5, mT5, BioBART, etc.

This repository contains various models for text summarization tasks. Each model has a separate directory containing the implementation code, pretrained weights, and a Jupyter notebook for testing the model on sample input texts. Feel free to use these models for your own text summarization tasks or to experiment with them further.

The goal of this project is to design a classifier to use for sentiment analysis of product reviews. Our training set consists of reviews written by Amazon customers for various food products. The reviews, originally given on a 5 point scale, have been adjusted to a +1 or -1 scale, representing a positive or negative review, respectively.

newsletter is a web-app that automatically generates newsletters and blog posts using Machine Learning

LongPegasus package is used for inducing longformer self attention over base pegasus abstractive summarization model to increase the token limit and performance.

Summarizing YouTube Video Transcripts & Blog Posts with HuggingFace Transformers.

Summary and Topic Modeling techniques such as LDA applied to the BBC X-Sum dataset

Abstractive summarization of Reddit datasets with Transformers.

ELSA combines extractive and abstractive approaches to the automatic text summarization

Transformer encoder-decoder (PEGASUS) pretraining and finetuning using Tensorflow + TFRecords on TPU

This repository explores the use of advanced sequence-to-sequence networks and transformer models, such as BERT, BART, PEGASUS, and T5, for summarizing multi-text documents in the medical domain. It leverages extensive datasets like CORD-19 and a Biomedical Abstracts dataset from Hugging Face to fine-tune these models.

The News Text Summarization project aims to develop a platform that automatically generates concise and accurate summaries of Al Jazeera articles. By utilizing web scraping and advanced models like T5, BART, and PEGASUS, we explore translation and fine-tuning approaches to produce summaries in the target language. Our goal is to provide users with

Microsoft Edge Extention which paraphrase text.

Fine Tuning pegasus and flan-t5 pre-trained language model on dialogsum datasets for conversation summarization to to optimize context window in RAG-LLMs

Text Summarizer is an NLP-driven application designed to provide concise summaries of large text documents. Leveraging advanced algorithms and machine learning techniques, this project streamlines the process of information retrieval, making it easier for users to grasp key points quickly.

Abstractive multi-document summarization pipeline over the Multi-News dataset using three state-of-the-art seq2seq models: Pegasus-Large, BART-Large-CNN, and LED-Base-16384