Awesome Open Source
Awesome Open Source

Text Summarization models

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

Blogs

This repo has been explained in a series of Blogs


Try out this text summarization through this website (eazymind) , eazymind which enables you to summarize your text through

  • 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}"
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))

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://awesomeopensource.com/project/Currie32/Text-Summarization-with-Amazon-Reviews seq2seq

Model 2

1- Model_2/Model_2.ipynb

a modification to https://awesomeopensource.com/project/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/thomasschmied/Text_Summarization_with_Tensorflow/blob/master/summarizer_amazon_reviews.ipynb


Implementation B (Pointer Generator seq2seq network)

it is a continuation of the amazing work of https://awesomeopensource.com/project/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


Implementation C (Reinforcement Learning For Sequence to Sequence )

this implementation is a continuation of the amazing work done by https://awesomeopensource.com/project/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}
}

Model 5 RL

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

zaksum_eval.ipynb

built by python3 for evaluation

Results/Reinforcement Learning

  • output from Model 5 RL used as input to the zaksum_eval.ipynb
Related Awesome Lists
Top Programming Languages
Top Projects

Get A Weekly Email With Trending Projects For These Topics
No Spam. Unsubscribe easily at any time.
Jupyter Notebook (154,306
Machine Learning (37,105
Deep Learning (36,492
Text (33,050
Tutorials (23,565
Tensorflow (22,344
Artificial Intelligence (18,900
Natural Language Processing (14,766
Reinforcement Learning (4,436
Recurrent Neural Networks (4,366
Word2vec (2,246
Summarization (1,360
Sequence To Sequence (1,344
Encoder Decoder (441
Text Summarization (381
Rouge (375
Policy Gradient (296
Google Colab (260
Google Colaboratory (76
Abstractive Text Summarization (22
Pointer Generator (17