Tawmo2026-10-082026-10-082022http://nits.ndl.gov.in/handle/123456789/191Abstractive Text Summarization is a burgeoning natural language processing task that has seen with the deep learning models. With the soaring amassment Of un- structured text data, text summarization has found applications in many domains such as law, medicine and news. The growth in research interest in developing better sum- marization systems, more fluent human-like summaries, and significant, more refined datasets have offered to test natural language processing models to improve existing model performance. Even so, no significant improvements are made while evaluating the machine summaries, still relying on metrics such as BLEU and ROUGE which only performs syntactic evaluation and cannot evaluate based On semantic equivalence. Semantic Textual Similarity is a Natural Language Processing (NLP) task that uses a predetermined metric to score the relationship between texts or documents. Measuring semantic similarity scores between texts is useful in many applications in Natural Language Processing (NLP) and fields. In this work, we propose a novel evaluation method to semantically analyse and measure the quality of the abstractive text summaries. The proposed model compared the resultant output across the datasets to determine the proficiency of the model and the datasets with regards to ROUGE, BLEU scores and the baseline system.enThesis