AI Based Text Summarisation Observation Method and Process Using NLP

Abstract

The rapid growth of digital textual information has created significant challenges in information management, retrieval, and decision-making processes. Artificial Intelligence (AI)-based text summarisation has emerged as an effective solution for condensing large volumes of textual data into concise and meaningful summaries while preserving the essential semantic content. This paper presents a comprehensive study of text summarisation observation methods and processing mechanisms using Natural Language Processing (NLP). The study examines the evolution of summarisation techniques from traditional extractive approaches to advanced transformer-based and Large Language Model (LLM)-driven abstractive frameworks. The proposed observation methodology focuses on systematic stages including text acquisition, preprocessing, linguistic analysis, feature extraction, semantic representation, summary generation, and evaluation. Furthermore, the paper discusses the integration of deep learning architectures, attention mechanisms, and contextual language models for improving summary quality, coherence, and factual consistency. The research highlights current challenges such as redundancy reduction, semantic preservation, multilingual processing, and evaluation reliability. The findings demonstrate that AI-driven NLP frameworks significantly enhance summarisation efficiency and accuracy, making them suitable for applications in education, healthcare, legal analytics, business intelligence, and scientific research. The study also identifies future research directions toward explainable, adaptive, and domain-aware summarisation systems.