TY - JOUR AU - G Blessy AU - PROF. CH. GVN. PRASAD PY - 2026 DA - 2026/06/15 TI - OCE: GPT Contextual Text Extraction and Summarization JO - Global Journal of Engineering Innovations and Interdisciplinary Research VL - 6 IS - 4 AB - The exponential growth of digital information has created significant challenges in extracting meaningful knowledge from large volumes of unstructured textual data. Organizations, researchers, government agencies, healthcare institutions, and enterprises continuously generate documents including research articles, reports, emails, legal records, medical notes, news articles, and technical documentation. Manual analysis of these documents is time-consuming, labor-intensive, and often impractical for real-time decision-making. Recent advances in Generative Artificial Intelligence and Large Language Models (LLMs), particularly Generative Pre-trained Transformers (GPT), have significantly improved contextual language understanding, enabling intelligent extraction of relevant information and generation of coherent summaries. Unlike traditional extractive summarization techniques, GPT-based contextual summarization captures semantic relationships, contextual dependencies, and document intent while producing concise and human-readable summaries. This paper proposes OCE (GPT Contextual Text Extraction and Summarization), an intelligent framework that combines contextual information extraction, semantic representation learning, transformer-based language modeling, and abstractive summarization for efficient document understanding. The proposed framework performs document preprocessing, contextual embedding generation, key information extraction, semantic ranking, GPTbased summarization, and quality evaluation. By utilizing contextual understanding instead of simple keyword matching, OCE produces accurate summaries while preserving important semantic information contained within lengthy documents. Experimental evaluation was conducted using benchmark datasets comprising research articles, news reports, legal documents, healthcare records, and technical documents. Comparative analysis demonstrates that the proposed OCE framework achieves superior contextual understanding, summarization quality, semantic relevance, and computational efficiency compared with conventional extractive and transformer-based summarization methods. The proposed architecture provides an intelligent document understanding solution suitable for large-scale knowledge management, information retrieval, and decision support applications. SN - 3066-1226 UR - https://dx.doi.org/10.33425/3066-1226.1306 DO - 10.33425/3066-1226.1306