The task of generating long narratives using Large Language Models (LLMs) is a largely unexplored area within natural language processing (NLP). Although modern LLMs can handle up to 1 million tokens, ensuring coherence and control over long story generation is still a significant challenge. This paper investigates the use of summarization techniques to create extended narratives, specifically targeting long stories. We propose a special prompting scheme that segments the narrative into several parts and chapters, each generated iteratively with contextual information. Our approach is evaluated with GAPELMAPER, a sophisticated text coherence metric, for automatic evaluation to maintain the structural integrity of the generated stories. We also rely on human evaluation to assess the quality of the generated text. This research advances the development of tools for long story generation in NLP, highlighting both the potential and current limitations of LLMs in this field.