if you are able to endorse me on Arxiv, i would be more than glad https://arxiv.org/auth/endorse?x=FRBB89 thanks This repo is built to collect multiple implementations for abstractive approaches to address text summarization , for different languages (Hindi, Amharic, English, and soon isA Arabic)
If you found this project helpful please consider citing our work, it would truly mean so much for me
@INPROCEEDINGS{9068171,
author={A. M. {Zaki} and M. I. {Khalil} and H. M. {Abbas}},
booktitle={2019 14th International Conference on Computer Engineering and Systems (ICCES)},
title={Deep Architectures for Abstractive Text Summarization in Multiple Languages},
year={2019},
volume={},
number={},
pages={22-27},}
@misc{zaki2020amharic,
title={Amharic Abstractive Text Summarization},
author={Amr M. Zaki and Mahmoud I. Khalil and Hazem M. Abbas},
year={2020},
eprint={2003.13721},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
it is built to simply run on google colab , in one notebook 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 , and you don't have to download data to your device as we connect these jupiter notebooks to google drive
- Arabic Summarization Model using the corner stone implemtnation (seq2seq using Bidirecional LSTM Encoder and attention in the decoder) for summarizing Arabic news
- implementation A Corner stone seq2seq with attention (using bidirectional ltsm ) , three different models for this implemntation
- implementation B seq2seq with pointer genrator model
- implementation C seq2seq with reinforcement learning
This repo has been explained in a series of Blogs
- to understand how to work with google colab eco system , and how to integrate it with your google drive , this blog can prove useful DeepLearning Free Ecosystem
- Tutorial 1 Overview on the different appraches used for abstractive text summarization
- Tutorial 2 How to represent text for our text summarization task
- Tutorial 3 What seq2seq and why do we use it in text summarization
- Tutorial 4 Multilayer Bidirectional Lstm/Gru for text summarization
- Tutorial 5 Beam Search & Attention for text summarization
- Tutorial 6 Build an Abstractive Text Summarizer in 94 Lines of Tensorflow
- Tutorial 7 Pointer generator for combination of Abstractive & Extractive methods for Text Summarization
- Tutorial 8 Teach seq2seq models to learn from their mistakes using deep curriculum learning
- Tutorial 9 Deep Reinforcement Learning (DeepRL) for Abstractive Text Summarization made easy
- Tutorial 10 Hindi Text Summarization
Try out this text summarization through this website (eazymind) ,
- curl call
curl -X POST
http://eazymind.herokuapp.com/arabic_sum/eazysum
-H 'cache-control: no-cache'
-H 'content-type: application/x-www-form-urlencoded'
-d "eazykey={eazymind api key}&sentence={your sentence to be summarized}"
- python package (pip install eazymind)
pip install eazymind
from eazymind.nlp.eazysum import Summarizer
#---key from eazymind website---
key = "xxxxxxxxxxxxxxxxxxxxx"
#---sentence to be summarized---
sentence = """(CNN)The White House has instructed former
White House Counsel Don McGahn not to comply with a subpoena
for documents from House Judiciary Chairman Jerry Nadler,
teeing up the latest in a series of escalating oversight
showdowns between the Trump administration and congressional Democrats."""
summarizer = Summarizer(key)
print(summarizer.run(sentence))
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
is a modification on of David Currie's https://github.com/Currie32/Text-Summarization-with-Amazon-Reviews seq2seq
a modification to https://github.com/dongjun-Lee/text-summarization-tensorflow
a modification to Model 2.ipynb by using concepts from http://www.aclweb.org/anthology/K16-1028
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
a modification to https://github.com/thomasschmied/Text_Summarization_with_Tensorflow/blob/master/summarizer_amazon_reviews.ipynb
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
uses a pointer generator with seq2seq with attention it is built using python2.7
built by python3 for evaluation
- 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
this implementation is a continuation of the amazing work done by https://github.com/yaserkl/RLSeq2Seq https://arxiv.org/abs/1805.09461
@article{keneshloo2018deep,
title={Deep Reinforcement Learning For Sequence to Sequence Models},
author={Keneshloo, Yaser and Shi, Tian and Ramakrishnan, Naren and Reddy, Chandan K.},
journal={arXiv preprint arXiv:1805.09461},
year={2018}
}
this is a library for building multiple approaches using Reinforcement Learning with seq2seq , i have gathered their code to run in a jupiter notebook , and to access google drive built for python 2.7
built by python3 for evaluation
- output from Model 5 RL used as input to the zaksum_eval.ipynb