Does adding a list of Word2Vec embeddings give a meaningful represenation?

I'm using a pre-trained word2vec model (word2vec-google-news-300) to get the embeddings for a given list of words. Please note that this is NOT a list of words that we get after tokenizing a sentence, it is just a list of words that describe a given image.

Now I'd like to get a single vector representation for the entire list. Does adding all the individual word embeddings make sense? Or should I consider averaging? Also, I would like the vector to be of a constant size so concatenating the embeddings is not an option.

It would be really helpful if someone can explain the intuition behind considering either one of the above approaches.

Averaging is most typical, when someone is looking for a super-simple way to turn a bag-of-words into a single fixed-length vector.

You could try a simple sum, as well.

But note that the key difference between the sum and average is that the average divides by the number of input vectors. Thus they both result in a vector that's pointing in the exact same 'direction', just of different magnitude. And, the most-often-used way of comparing such vectors, cosine-similarity, is oblivious to magnitudes. So for a lot of cosine-similarity-based ways of later comparing the vectors, sum-vs-average will give identical results.

On the other hand, if you're comparing the vectors in other ways, like via euclidean-distances, or feeding them into other classifiers, sum-vs-average could make a difference.

Similarly, some might try unit-length-normalizing all vectors before use in any comparisons. After such a pre-use normalization, then:

• euclidean-distance (smallest to largest) & cosine-similarity (largest-to-smallest) will generate identical lists of nearest-neighbors
• average-vs-sum will result in different ending directions - as the unit-normalization will have upped some vectors' magnitudes, and lowered others, changing their relative contributions to the average.

What should you do? There's no universally right answer - depending on your dataset & goals, & the ways your downstream steps use the vectors, different choices might offer slight advantages in whatever final quality/desirability evaluation you perform. So it's common to try a few different permutations, along with varying other parameters.

Separately:

• The GoogleNews vectors were trained on news articles back around 2013; their word senses thus may not be optimal for an image-labeling task. If you have enough of your own data, or can collect it, training your own word-vectors might result in better results. (Both the use of domain-specific data, & the ability to tune training parameters based on your own evaluations, could offer benefits - especially when your domain is unique, or the tokens aren't typical natural-language sentences.)
• There are other ways to create a single summary vector for a run-of-tokens, not just arithmatical-combo-of-word-vectors. One that's a small variation on the word2vec algorithm often goes by the name Doc2Vec (or 'Paragraph Vector') - it may also be worth exploring.
• There are also ways to compare bags-of-tokens, leveraging word-vectors, that don't collapse the bag-of-tokens to a single fixed-length vector 1st - and while they're more expensive to calculate, sometimes offer better pairwise similarity/distance results than simple cosine-similarity. One such alternate comparison is called "Word Mover's Distance" - at some point,, you may want to try that as well.