The increasing amount of digital content necessitates advanced natural language processing techniques to support user understanding and informed decision-making. This paper investigates the integration of textual entailment, determining whether a given hypothesis logically follows from a textual premise, and abstractive summarization, which generates concise summaries by rephrasing content in a coherent and context-aware manner. By combining these approaches, the paper aims to enhance automated systems’ capacity to infer implicit semantic relationships and produce tailored summaries that align with user intent. The paper provides a structured review of the evolution, strengths, and limitations of these techniques, with emphasis on their application in domains such as education, healthcare, and information retrieval. It also identifies key challenges in their integration and outlines future directions for developing adaptive systems that more effectively address user needs across diverse information environments.