• Syed Ufaq Chashoo
• Kamal Kumar

Automatic Text Summarization is the process of generating automatic summaries from a document by extracting its principal statements and preserving the overall semantics of the text at the same time. Text Summarization is a potential solution to the problem of information overload the world is facing at the moment. Numerous text summarizers already exist in the literature, but they fail to preserve the semantics of the text. This research uses this semantic feature as a fundamental technique to find high quality summaries of documents. We use the distributional Semantic Model called word2Vec and compare the results with other state-of-the-art summarizers. We evaluated our results using ROUGE on DUC 2007 dataset. This system outperformed all other reference summarizers proving that using semantics as a feature increases the efficiency of the system.

Text Summarization, word2Vec, Distributional Semantic Model

"Semantic Text Summarization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 9, page no.c664-c671, September-2021, Available :http://www.jetir.org/papers/JETIR2109289.pdf

"Semantic Text Summarization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 9, page no. ppc664-c671, September-2021, Available at : http://www.jetir.org/papers/JETIR2109289.pdf