I'm working on a project to extract books from Hacker News. I've previously found book recommendations for Ask HN Books, and have used the Work of Art named entity from Ontonotes to detect the titles. Another approach is to use extractive question answering as a sort of zero-shot NER. This works amazingly well, at least providing that there is an actual book title there.

The code is simple using Transformers high level Question Answering Pipeline. I picked the first question that came to mind; some prompt engineering may produce better results.

from transformers import pipeline

pipe(context=books[0],
handle_impossible_answer=True)
I found contextualising the author could help; first find a book name then ask "Who is the author of <book>?". Subjectively this worked better than the Work of Art NER when there was a book. If you want to see some examples check out the example notebook.