not really, vectorising text/books is old school ML by this point.
at least to me that seems the same as https://en.wikipedia.org/wiki/Word2vec for e.g.
Sure, the idea of making a vector embedding for words, sentences, documents etc. is old, but the meat is in how you construct this embedding. I think embeddings have gotten quite a bit better since word2vec.
Well... Everything new is old "A vector space model for automatic indexing" 1975 - https://dl.acm.org/doi/10.1145/361219.361220