Pass 2 different kinds of X training data to ML model simultaneously
You could just apply two independent vectorization steps on your input X (one vectorizer for description and another for summary) and then concatenate the obtained feature matrices into a single feature matrix.

Doing it this way you will have features such as description_"such", description_"long", ..., summary_"bombay", summary_"1971", ..., so any model you apply will be able to:


Use all the features from the description and summary altogether
Give a different weight to desctiption tokens and summary tokens