Pointer-Generator Networks with Different Word Embeddings for Abstractive Summarization
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Updated
Jun 25, 2021 - Python
Pointer-Generator Networks with Different Word Embeddings for Abstractive Summarization
Text Summarization using Residual Logarithmic LSTMs
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
Recalculating ROUGE scores for See et al. (2017) test outputs.
Towards more abstractive text summarization with Pointer-Generator Networks
UCL Statistical Natural Language Process Group Project. Text summarization with Seq-2-seq, pointer generator, SeqGAN and PointerGAN.
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 )
Code implementation for the Paper "Making a Point: Pointer-Generator Transformers for Disjoint Vocabularies" (https://www.aclweb.org/anthology/2020.aacl-srw.13.pdf).
Code for Master's Thesis on 'Neural Automatic Summarization' written at the IT University of Copenhagen
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
Text summarization methods introduction
Pytorch implementation of "Get To The Point: Summarization with Pointer-Generator Networks"
Text Summarizer implemented in PyTorch
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
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