Multiple implementations for abstractive text summurization , using google colab
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Updated
Oct 6, 2020 - Jupyter Notebook
Multiple implementations for abstractive text summurization , using google colab
Deep Reinforcement Learning For Sequence to Sequence Models
A Abstractive Summarization Implementation with Transformer and Pointer-generator
My seq2seq based on tensorflow
The pytorch implementation of Get To The Point: Summarization with Pointer-Generator Networks.
Datasets I have created for scientific summarization, and a trained BertSum model
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.
Corner stone seq2seq with attention (using bidirectional ltsm )
Code for Master's Thesis on 'Neural Automatic Summarization' written at the IT University of Copenhagen
This repository contains the data and code for the paper "Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process Priors" (SPNLP@ACL2022)
An Implementation of Copy Seq2Seq
Pytorch implementation of Get To The Point: Summarization with Pointer-Generator Networks (2017) by Abigail See et al.
Pointer-Generator Networks with Different Word Embeddings for Abstractive Summarization
Text Summarizer implemented in PyTorch
Text Summarization using Residual Logarithmic LSTMs
Pytorch implementation of the ACL paper 'Get To The Point: Summarization with Pointer-Generator Networks (See et al., 2017)', adapted to a Korean dataset
Pytorch implementation of "Get To The Point: Summarization with Pointer-Generator Networks"
Text summarization methods introduction
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