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Extractive summarization is the key to alleviating the problem of information overload by choosing essential sentences from a document and creating short summaries. In this paper, a new extractive summarization method that combines three different scoring mechanisms, including term frequency-inverse document frequency (TF-IDF), sentence positional weighting, and semantic similarity with the document title, is proposed. In contrast to traditional techniques based on statistical or graph-based models, our method utilizes a hybrid approach to best capture sentence salience and contextual utility. Experimental evaluations on benchmark data show that the proposed method has competitive ROUGE scores against strong baselines while being interpretable and of low computational cost. This method provides a light but efficient solution for real-time summarization operations and provides a basis for future developments that include semantic comprehension and deep learning integration.
Nath et al. (Sat,) studied this question.