summarytokenizer.py

#preparing a tokenizer for summary on training data 
y_tokenizer = Tokenizer()
y_tokenizer.fit_on_texts(list(y_tr))

#convert summary sequences into integer sequences
y_tr    =   y_tokenizer.texts_to_sequences(y_tr) 
y_val   =   y_tokenizer.texts_to_sequences(y_val) 

#padding zero upto maximum length
y_tr    =   pad_sequences(y_tr, maxlen=max_len_summary, padding='post')
y_val   =   pad_sequences(y_val, maxlen=max_len_summary, padding='post')

y_voc_size  =   len(y_tokenizer.word_index) +1