Pointer-Generator Networks with Different Word Embeddings for Abstractive Summarization
-
Updated
Jun 25, 2021 - Python
Pointer-Generator Networks with Different Word Embeddings for Abstractive Summarization
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.
Gated Attention Reader for Cloze style question answering and reading comphrehension, implemented in PyTorch
AACL'2022: Unsupervised Single Document Abstractive Summarization using Semantic Units
NLP CNN Daily Mail News Abstractive Text Summarisation
Code to obtain raw texts of the CNN / Daily Mail dataset (non-anonymized) for summarization (python3)
A script to process non-anonymized CNN and DailyMail for summary.
Abstractive text summarization using CNN/DailyMail Dataset training on RNN/LSTM & T5.
non-anonymized cnn/dailymail dataset for text summarization
Neural abstractive summarization (seq2seq + copy (or pointer network) + coverage) in pytorch on CNN/Daily Mail
Add a description, image, and links to the cnn-dailymail topic page so that developers can more easily learn about it.
To associate your repository with the cnn-dailymail topic, visit your repo's landing page and select "manage topics."