Neural abstractive summarization (seq2seq + copy (or pointer network) + coverage) in pytorch on CNN/Daily Mail

Abstractive text summarization using CNN/DailyMail Dataset training on RNN/LSTM & T5.

non-anonymized cnn/dailymail dataset for text summarization

A script to process non-anonymized CNN and DailyMail for summary.

Code to obtain raw texts of the CNN / Daily Mail dataset (non-anonymized) for summarization (python3)

NLP CNN Daily Mail News Abstractive Text Summarisation

AACL'2022: Unsupervised Single Document Abstractive Summarization using Semantic Units

Gated Attention Reader for Cloze style question answering and reading comphrehension, implemented in PyTorch

News summarization using T5 model

A transformer-based models to enhance text summarization with a focus on rare and infrequently used words built with CNN/DailyMail Dataset using PyTorch, NLTK, Bidirectional Auto-Regressive Transformers(BART)

This repository contains an implementation of a text summarization model using the T5 (Text-To-Text Transfer Transformer) architecture. The goal of this project is to build a system that can generate concise summaries from lengthy news articles, specifically using the CNN/DailyMail dataset.

Pointer-Generator Networks with Different Word Embeddings for Abstractive Summarization

Fine-tuning T5-small on the CNN/DailyMail dataset for abstractive text summarization using Hugging Face Transformers and PyTorch. Includes dataset preprocessing, training, evaluation with ROUGE metrics, and inference scripts for generating concise news summaries.