Get to the point - Pointer Generator Network
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Updated
Jul 30, 2024 - Python
Get to the point - Pointer Generator Network
An improved implementation of Beam Search Decoding in RNN-based Seq2Seq Architecture
The pytorch implementation of Get To The Point: Summarization with Pointer-Generator Networks.
Deep Reinforcement Learning For Sequence to Sequence Models
Генерация новостных заголовков
# Comparing the performance of LSTM and GRU for Text Summarization using Pointer Generator Networks
This repository contains the data and code for the paper "Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process Priors" (SPNLP@ACL2022)
Code for Master's Thesis on 'Neural Automatic Summarization' written at the IT University of Copenhagen
UCL Statistical Natural Language Process Group Project. Text summarization with Seq-2-seq, pointer generator, SeqGAN and PointerGAN.
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"
Pointer-Generator Networks with Different Word Embeddings for Abstractive Summarization
A Abstractive Summarization Implementation with Transformer and Pointer-generator
Tensorflow 2.0 implementation of the Pointer-Generator network from the "Get to the Point" article (https://arxiv.org/abs/1704.04368)
An Abstractive Summarization(for Datasets in English format) Implementation with Transformer and Pointer-generator
Code implementation for the Paper "Making a Point: Pointer-Generator Transformers for Disjoint Vocabularies" (https://www.aclweb.org/anthology/2020.aacl-srw.13.pdf).
Multiple implementations for abstractive text summurization , using google colab
Text summarization methods introduction
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.
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