Keyphrase generation, the art of automatically identifying the core concepts within a text, has always been a tricky balancing act. How do you ensure the generated keyphrases are both relevant (high precision) and comprehensively capture the main ideas (high recall)? Traditional methods often struggle to achieve both simultaneously. This new research explores a groundbreaking 'generate-then-select' framework that combines the strengths of two powerful approaches: ONE2SET and Large Language Models (LLMs). ONE2SET, known for its high recall, acts as the generator, creating a wide range of potential keyphrases. Then, an LLM steps in as the selector, leveraging its advanced semantic understanding to filter out less relevant candidates. The researchers further enhanced this framework with two key innovations. First, they introduced an 'Optimal Transport-based assignment' strategy to improve the training of the ONE2SET generator, ensuring it produces even more accurate candidates. Second, they reframed the selection process as a sequence labeling task for the LLM. This allows the LLM to consider the relationships between selected keyphrases, minimizing redundancy and maximizing coherence. The results on multiple benchmark datasets are impressive, showing significant improvements, especially in identifying keyphrases not explicitly mentioned in the text (absent keyphrases). This new method offers a compelling solution to the long-standing challenge of balancing precision and recall in keyphrase generation. By combining the strengths of ONE2SET and LLMs, it paves the way for more accurate and nuanced understanding of textual content, which can have significant implications in various domains, from information retrieval to text summarization. Future research aims to unify the generation and selection stages into a single, more integrated model, potentially further boosting performance and efficiency.