I have used a text generation library called Texar , Its a beautiful library with a lot of abstractions, i would say it to be scikit learn for text generation problems.
The main idea behind this architecture is to use the transfer learning from pretrained BERT a masked language model , I have replaced the Encoder part with BERT Encoder and the deocder is trained from the scratch.
One of the advantages of using Transfomer Networks is training is much faster than LSTM based models as we elimanate sequential behaviour in Transformer models.
Transformer based models generate more gramatically correct and coherent sentences.
wget https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-12_H-768_A-12.zip unzip uncased_L-12_H-768_A-12.zipPlace the story and summary files under data folder with the following names. -train_story.txt -train_summ.txt -eval_story.txt -eval_summ.txt each story and summary must be in a single line (see sample text given.)
Step1: Run Preprocessing python preprocess.py
This creates two tfrecord files under the data folder.
Step 2: python main.py
Configurations for the model can be changes from config.py file
Step 3: Inference Run the command python inference.py This code runs a flask server Use postman to send the POST request @http://your_ip_address:1118/results with two form parameters story,summary