What are some alternatives to the doc2vec embedding model? I.e models that convert paragraphs/documents into vectors, not just models that take the mean/sum of the word embeddings of each word in the document.
Depending on your target task. If you are to classify documents, then e.g. fastText has it's own approach and there are other classification techniques, not strictly generating embeddings, like LSA / LDA (using topic modelling) or word mover distance.