Automatic Report Generation Based on Text Summarization Meets Data Mining

The combination of Automatic Report Generation, Text Summarization, Data Mining, Natural Language Processing (NLP), and Clustering emerges as a powerful paradigm to extract meaningful insights from large, complicated datasets in the era of abundant information. This study investigates a comprehensive system that combines sophisticated clustering algorithms, cutting-edge NLP approaches, and the complex interactions between data mining and text summarization. The method performs abstractive summarising by utilising pre-trained language models such as GPT-3 and BERT, which capture subtle linguistic patterns to provide coherent and contextually rich summaries. Text and numeric data clustering techniques help to further identify patterns and links in datasets, which improves the system's ability to produce insightful results. A feedback loop-driven iterative refinement approach guarantees adaptation to evolving data landscapes. Applications across many industries highlight the adaptability and usefulness of the suggested methodology. Ethical issues are discussed, interpretability, and directions for further study, establishing this integrated method as a revolutionary force in data-driven decision-making.