"Once the dictionary is created, we can average all the word vectors for a given headline to get the numeric representation of the headline itself." Can someon

"Once the dictionary is created, we can average all the word vectors for a given headline to get the numeric representation of the headline itself." Can someone explain me why this is useful? Aren't we losing a lot of precision from word2vec results? And as a general question: is there any useful knowledge we can extract from this vizualisation apart: most of the time, news channels write about different things? Don't get me wrong, I think the techniques displayed here are really cool, but I have the feeling the conclusions are either absent or trite.