The primary goal of text summarisation is to distil the most important details and turn them into a brief version that still captures the source material’s main points. As the volume of material in a document goes up, it becomes harder to summarize it manually, making automatic summarisation the better alternative. It is essential for email filtering, producing reports, collecting news, following events, and condensing user-generated material. The main goal of this work is to improve how a summary is built by selecting sentences with the greatest share of both concrete and abstract nouns. Emotions and ideas are revealed by using abstract nouns and concrete nouns to express things you can touch or see. We propose that Part of speech (POS) tagging should be performed to highlight and identify noun types and the most informative sentences can be picked by examining their noun density. Each story in the collection is listed in order of the length and only its earliest half is included in the summary. The overall compression ratio for this technique is 54.57%. Precision, recall, and F1-score have been used for evaluation to compare the automatically generated summaries with gold summaries and the results have been found to be 0.0497, 0.4202, and 0.0662. To measure its effectiveness this approach has bee compared to recent benchmarks including NarraSum and StorySumm. The research reveals that using noun concreteness can create better and more useful summaries.