I am working on an automated insights generation use case where I want to generate meaningful sentences from given aggregated data.
For example,
Data:
Student = John
Total_Marks = 96
Class_Average = 85
NLG model-generated insights:
1. You did an excellent job, John! Your score is 96!
2. You have scored 11 marks above the class average.
When I look at classic NLG, they are sentence generation approaches given a starting letter or word. This might be more of a Neural Machine Transition use case.
What do you think my approach should be?
