A Novel Optimized Language-Independent Text Summarization Technique

A substantial amount of textual data is present electronically in several languages. These texts directed the gear to information redundancy. It is essential to remove this redundancy and decrease the reading time of these data. Therefore, we need a computerized text summarization technique to extract relevant information from group of text documents with correlated subjects. This paper proposes a language-independent extractive summarization technique. The proposed technique presents a clustering-based optimization technique. The clustering technique determines the main subjects of the text, while the proposed optimization technique minimizes redundancy, and maximizes significance. Experiments are devised and evaluated using BillSum dataset for the English language, MLSUM for German and Russian and Mawdoo3 for the Arabic language. The experiments are evaluated using ROUGE metrics. The results showed the effectiveness of the proposed technique compared to other language-dependent and language-independent summarization techniques. Our technique achieved better ROUGE metrics for all the utilized datasets. The technique accomplished an F-measure of 41.9% for Rouge-1, 18.7% for Rouge-2, 39.4% for Rouge-3, and 16.8% for Rouge-4 on average for all the dataset using all three objectives. Our system also exhibited an improvement of 26.6%, 35.5%, 34.65%, and 31.54% w.r.t. The recent model contributed in the summarization of BillSum in terms of ROUGE metric evaluation. Our model’s performance is higher than the compared models, especially in the metric results of ROUGE_2 which is bi-gram matching.