The problem I’m trying to solve, in the most general sense, is that you’re given a set of documents and each document in your set has specific information that you’re trying to pull out.  Examples of specific information to pull out include:

Author of the document
When was the document written
Who is the recipient of the document
etc.

And keep in mind information in the document may not always be stated in an obvious way.  In one document, the author may be given as “Author: Joe Shmo”.  In another one it might say: “From Jane Doe”.  But let’s assume that every document has an author and any normal adult human with an average comprehension of English can pull out the author even though the author may not be stated in the exact same way in each document (and let’s assume there are countless ways of saying who the author is and no reasonable Regex pattern can be used to pull it out.)  Ditto to the other fields like the date when it’s written and the intended recipient of the document.
I originally thought of using a Question Answering model as a basis for this task but it might be overkill.  Regarding the Resume example, I might end up training the model on just two questions and their respective answers for each resume in the dataset:

What is the education?
What is the experience?

I suppose training a custom NER model might be another route to take.