Does it make sense to use TF-IDF to extract most important tokens from a corpus? I have a collection of documents and I'd like to extract the most important words and phrases from the entire corpus.

My understanding of TF-IDF is that it is calculated per token per document, so the calculated weights are relative to a given document in the corpus. Is there a way to use TF-IDF to recover the most significant terms in the entire corpus, or is this the wrong approach? If the latter, what would be a more appropriate NLP approach?
Provided limited information &amp; context you have provided, I would suggest you to look for feature selection when each dimension belongs to a word. Feature selection will give you most important words. Most important words in the sense, words deciding the decision surface of the model.