Deep Reinforcement Learning For Sequence to Sequence Models

Multiple implementations for abstractive text summurization , using google colab

A Abstractive Summarization Implementation with Transformer and Pointer-generator

Datasets I have created for scientific summarization, and a trained BertSum model

My seq2seq based on tensorflow

The pytorch implementation of Get To The Point: Summarization with Pointer-Generator Networks.

Pytorch implementation of the ACL paper 'Get To The Point: Summarization with Pointer-Generator Networks (See et al., 2017)', adapted to a Korean dataset

This repository contains the data and code for the paper "Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process Priors" (SPNLP@ACL2022)

Pointer Generator Network: Seq2Seq with attention, pointing and coverage mechanism for abstractive summarization.

Pytorch implementation of Get To The Point: Summarization with Pointer-Generator Networks (2017) by Abigail See et al.

Pytorch implementation of Get To The Point: Summarization with Pointer-Generator Networks (2017) by Abigail See et al.

An Abstractive Summarization(for Datasets in English format) Implementation with Transformer and Pointer-generator

Text Summarizer implemented in PyTorch

Pytorch implementation of "Get To The Point: Summarization with Pointer-Generator Networks"

Text summarization methods introduction

An Implementation of Copy Seq2Seq

resources for the paper 'Get To The Point: Summarization with Pointer-Generator Networks' with python3.x. overview on the post http://www.abigailsee.com/2017/04/16/taming-rnns-for-better-summarization.html or

Генерация новостных заголовков

Code for Master's Thesis on 'Neural Automatic Summarization' written at the IT University of Copenhagen

The pointer-generator network does a better job at copying words from the source text. Additionally it also is able to copy out-of-vocabulary words allowing the algorithm to handle unseen words even if the corpus has a smaller vocabulary.