How does gensim calculate doc2vec paragraph vectors

Question

i am going thorugh this paper http://cs.stanford.edu/~quocle/paragraph_vector.pdf

and it states that

" Theparagraph vector and word vectors are averaged or concatenated to predict the next word in a context. In the experiments, we use concatenation as the method to combine the vectors."

How does concatenation or averaging work?

example (if paragraph 1 contain word1 and word2):

word1 vector =[0.1,0.2,0.3]
word2 vector =[0.4,0.5,0.6]

concat method 
does paragraph vector = [0.1+0.4,0.2+0.5,0.3+0.6] ?

Average method 
does paragraph vector = [(0.1+0.4)/2,(0.2+0.5)/2,(0.3+0.6)/2] ?

Also from this image:

It is stated that :

The paragraph token can be thought of as another word. It acts as a memory that remembers what is missing from the current context – or the topic of the paragraph. For this reason, we often call this model the Distributed Memory Model of Paragraph Vectors (PV-DM).

Is the paragraph token equal to the paragraph vector which is equal to on?

Answer 1

How does concatenation or averaging work?

You got it right for the average. The concatenation is: [0.1,0.2,0.3,0.4,0.5,0.6].

Is the paragraph token equal to the paragraph vector which is equal to on?

The "paragraph token" is mapped to a vector that is called "paragraph vector". It is different from the token "on", and different from the word vector that the token "on" is mapped to.