Can AI generate accurate keywords based on the content of the paper?

Modern artificial intelligence systems can generate relevant keywords from academic content, though accuracy depends on algorithmic design and contextual understanding. Effective keyword generation relies on natural language processing techniques like topic modeling (e.g., Latent Dirichlet Allocation) and contextual embedding analysis. High accuracy requires well-trained models on domain-specific corpora and clear, structured academic text. Performance varies across domains, heavily technical papers may pose challenges, and human verification remains essential to ensure semantic alignment and coverage of core themes. This application is particularly valuable for automating initial indexing, enhancing scholarly article discoverability in databases, and supporting systematic literature reviews. It significantly reduces manual effort in metadata creation while improving search efficiency, provided generated keywords undergo expert refinement to maintain precision and relevance.