Advanced Text Summarization Model Incorporating NLP Techniques and Feature-Based Scoring

The most common traditional approaches to summarizing large texts while retaining their importance are TF-IDF and TextRank. However, these methods often fail to retain narrative coherence and accuracy. This study’s improved summarization methodology overcomes these limitations by combining the linguistic and semantic resources. Moreover, although it is more computationally complex, it efficiently combines higher quality with faster summarization. Specifically, a method relies on a weighted feature score scheme. For example, various textual features such as Named Entity Counts, Noun Counts, and Sentence Position contribute to the summarization quality appropriately. This study’s summarization algorithm was tested using the CNN, XSum and BBC Summarization datasets, which aggregate documents from different areas. The methodology was checked against traditional methods using ROUGE-1 and ROUGE-2, ROUGE-L and BERTScore. The last one, BERTScore, evaluates the semantic similarity of the generated summaries and the references. This study shows that the proposed methodology generates summaries that are not only informative but even semantically faithfully reproduce the original textual information; it achieves high scores in terms of F1-measure across different evaluations like BERTSCORE (0.8857) and ROUGE-1(0.6388), ROUGE-2(0.5662) and ROUGE-L (0.6421). It thus suggests that the approach is applicable in real-life situations and deserves further research.