Within recent times, there has been a need for text summary generators to cut short lengthy academic or non-academic texts for effective reading. In recent times, there have been many techniques that deploy text summarization yet, their speed, efficiency and scalability is a concern. This is a challenge in natural language processing. The need for text summarization is necessary with the number of texts and documents which are available online. In this paper, we have proposed a new efficient technique of text summarization which uses text rank and lexical index scores to provide a coherent legible and concise text. Experimental results show that the technique is promising in solving the challenges faced by summarization systems in NLP. Furthermore, this technique can be extended further for generating bullet points, abstracts and mental maps with more semantic links.

Keywords : Text Mining, Summarization, Text Rank.