There are 1 repository under pointer-generator topic.
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
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.
An Abstractive Summarization(for Datasets in English format) Implementation with Transformer and Pointer-generator
Pytorch implementation of Get To The Point: Summarization with Pointer-Generator Networks (2017) by Abigail See et al.
Text Summarizer implemented in PyTorch
Pytorch implementation of "Get To The Point: Summarization with Pointer-Generator Networks"
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
An Implementation of Copy Seq2Seq
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.
Corner stone seq2seq with attention (using bidirectional ltsm )
UCL Statistical Natural Language Process Group Project. Text summarization with Seq-2-seq, pointer generator, SeqGAN and PointerGAN.
Tensorflow 2.0 implementation of the Pointer-Generator network from the "Get to the Point" article (https://arxiv.org/abs/1704.04368)
# Comparing the performance of LSTM and GRU for Text Summarization using Pointer Generator Networks
Text Summarization using Residual Logarithmic LSTMs
Recalculating ROUGE scores for See et al. (2017) test outputs.
Pointer-Generator Networks with Different Word Embeddings for Abstractive Summarization