Extractive Multi-document Text Summarization Leveraging Hybrid Semantic Similarity Measures

Because of the massive amount textual information accessible today, automated extraction text summarization is one most extensively used ways to organize information. The mechanisms help extract important topics data from a given set documents. Extractive method for providing representative summary by choosing pertinent sentences original text. multi-document systems' primary goal decrease quantity in document collection concentrating on crucial subjects and removing irrelevant material. In previous research, there are several methods such as term-weighting schemes similarity metrics constructing an system. There few studies that look at performance combining various Semantic word weighting techniques automatic summarization. We evaluated numerous semantic extractive this research. discussed we looked metrics. ROUGE have been evaluate model experiments using DUC datasets. Even more, combination formed different measures obtained highest results comparison with other models.