It leverages transformer-based embeddings to create semantic representations of both documents and candidate phrases, then uses cosine similarity to rank phrases by their relevance to the source. The library supports configurable embedding models and includes techniques like Max Sum Distance and Maximal Marginal Relevance to improve diversity in extracted keywords.