The exponential growth in the number of text documents produced daily on the web poses several difficulties to people who are responsible for collecting, organizing, and searching different textual content related to a particular topic. Automatic Text Summarization works well in this direction, which can review many documents and pull out the relevant information. But the limitations associated with automatic text summarization need to be removed by finding efficient workarounds. Although current research works have focused on this direction for further improvements, they still face many challenges. This paper proposes a combined semantic-based word and sentence similarity approach to summarize a corpus of text documents. To arrange the sentences in the final summary, KL-divergence technique is used. The experimental work is conducted using DUC datasets, and the obtained results are promising.