Text Summarization models

This repo is built to collect multiple implementations for abstractive approaches to address text summarization ,

it is built to simply run on google colab , in one notebook and to simply connect to your drive , so you would only need an internet connection to run these examples without the need to have a powerful machine , so all the code examples would be in a jupiter format .
to understand how to work with google colab eco system , and how to integrate it with your google drive , this blog can prove useful https://hackernoon.com/begin-your-deep-learning-project-for-free-free-gpu-processing-free-storage-free-easy-upload-b4dba18abebc

Implementation A (seq2seq with attention and feature rich representation)

contains 3 different models that implements the concept of hving a seq2seq network with attention also adding concepts like having a feature rich word representation This work is a continuation of these amazing repos

Model 1

is a modification on of David Currie's https://github.com/Currie32/Text-Summarization-with-Amazon-Reviews seq2seq

Model 2

1- Model_2/Model_2.ipynb

a modification to https://github.com/dongjun-Lee/text-summarization-tensorflow

2- Model_2/Model 2 features(tf-idf , pos tags).ipynb

a modification to Model 2.ipynb by using concepts from http://www.aclweb.org/anthology/K16-1028

Results

A folder contains the results of both the 2 models , from validation text samples in a zaksum format , which is combining all of

bleu
rouge_1
rouge_2
rouge_L
rouge_be for each sentence , and average of all of them

Model 3

a modification to https://github.com/theamrzaki/text_summurization_abstractive_methods/blob/master/Model_3.ipynb

Implementation B (Pointer Generator seq2seq network)

it is a continuation of the amazing work of https://github.com/abisee/pointer-generator https://arxiv.org/abs/1704.04368 this implementation uses the concept of having a pointer generator network to diminish some problems that appears with the normal seq2seq network

Model_4_generator_.ipynb

uses a pointer generator with seq2seq with attention it is built using python2.7

zaksum_eval.ipynb

built by python3 for evaluation

Results/Pointer Generator

output from generator (article / reference / summary) used as input to the zaksum_eval.ipynb
result from zaksum_eval

i will still work on their implementation of coverage mechanism , so much work is yet to come if God wills it isA

CodeDealer31/text_summurization_abstractive_methods