AI-driven text classification assigns predefined labels by learning patterns and context from labeled data. It relies on generalizable representations, principled decision boundaries, and interpretable constraints to improve accuracy. Data quality, bias, and transparency are central concerns, guiding evaluation and governance.…

AI-driven text summarization combines multi-step reasoning with learned discourse priors to produce concise representations of longer content. Modern architectures emphasize dense, hierarchical representations to preserve meaning while trimming extraneous material. The quality of summaries hinges on accuracy, coherence, and bias,…