Universal sentence encoder for multi sentence text similarity
As always, it depends on your data set. You can try it both ways and see which one gives the scores useful for your use case. In general, I have found that just feeding the whole text at one time to USE for text up to 100 words works just fine or even better. There is not a need to break into sentences and then average.
I am calculating similarity between 2 texts using universal sentence encoder
My question is whether embedding text at sentence level (which yields no of vectors equal to the no of sentences) and then average out scores instead of just creating a vector per text is a right way to do it?