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
Генерация новостных заголовков
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
Corner stone seq2seq with attention (using bidirectional ltsm )
Code for Master's Thesis on 'Neural Automatic Summarization' written at the IT University of Copenhagen
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