Is there a way to rank the Extracted Named Entities based on their importance/occurence in a document?
An easy way would be to use TF-IDF (term frequency–inverse document frequency). It can help you find how much terms stand out in a document (by comparing with your entire corpus) and use it to rank your entities.

TfidfVectorizer from scikit-learn

Just note that the TfidfVectorizer is on a word level. So some processing will be needed if your entities can consist of more than one word.

Alternatively you could use a model that allows you to produce a heatmap of the words. Then you can use that heatmap to look up your NEs in that heatmap. This paper, A Structured Self-Attentive Sentence Embedding, could give you some ideas.