I am trying to implement a text summarization model. I am using keras tensorflow anad I have used bert sentence embeddings and the output of the embeddings are feeded into an LSTM and then to a Dense layer with sigmoid activation function. I have used adam optimizer and binary crossentropy as the loss function.
The training y labels is a 2d-array i.e [array_of_documents][array_of_biniary_labels_foreach_sentence]
The problem is that during training, I am getting the training accuracy of around 0.22.
How can I improve my accuracy for the model?