9. The computer-implemented method as claimed in claim 1 , said method further comprising steps of: a. checking the document being processed to identify a prerequisite minimal size of data and/or word articles/words; b. storing the data or words in the document in a sequential order as per their occurrence in the document; c. creating two identical instances of the data to facilitate the identification of the Significant Single Value Term data set and the Significant Multi Value Term data set; d. determining a Significant Single Value Term from one of the instance of the data set and a Significant Multi Value Term from the other instance of the data set starting from the highest hand-in-hand words range predefined, followed by consecutive hand-in-hand range terms of lesser dimension; e. storing the identified Significant Single Value Term and the identified Significant Multi Value Term of different hand-in-hand range in their respective data sets; f. comparing words in the Significant Multi Value Term data set with the words in the Significant Single Value Term data set to extract those words in the respective hand-in-hand range of each Significant Multi Value Term data set as Best-Terms, which have at least one instance of Single Value Terms within their range and the rest of the hand-in-hand terms are decomposed; and g. comparing the data sets in such a way that every individual hand-in-hand range term that has at least one instance of any term in the Significant Single Value Term data set that is extracted as a Keyword and the rest are decomposed to determine the KeyTerms.
9. The computer-implemented method as claimed in claim 1 , said method further comprising steps of: a. checking the document being processed to identify a prerequisite minimal size of data and/or word articles/words; b. storing the data or words in the document in a sequential order as per their occurrence in the document; c. creating two identical instances of the data to facilitate the identification of the Significant Single Value Term data set and the Significant Multi Value Term data set; d. determining a Significant Single Value Term from one of the instance of the data set and a Significant Multi Value Term from the other instance of the data set starting from the highest hand-in-hand words range predefined, followed by consecutive hand-in-hand range terms of lesser dimension; e. storing the identified Significant Single Value Term and the identified Significant Multi Value Term of different hand-in-hand range in their respective data sets; f. comparing words in the Significant Multi Value Term data set with the words in the Significant Single Value Term data set to extract those words in the respective hand-in-hand range of each Significant Multi Value Term data set as Best-Terms, which have at least one instance of Single Value Terms within their range and the rest of the hand-in-hand terms are decomposed; and g. comparing the data sets in such a way that every individual hand-in-hand range term that has at least one instance of any term in the Significant Single Value Term data set that is extracted as a Keyword and the rest are decomposed to determine the KeyTerms. 13. The computer-implemented method as claimed in claim 9 , wherein the method for identifying the Significant Multi Value Term data set from the given document comprises steps of: a. retrieving words from the data set stored in sequential order as per their occurrences in the document; b. extracting hand-in-hand words of a predetermined range into appropriate data sets from retrieved words, thereafter extracting words of type Single Value Term that are left over by eliminating stop-words into a different data set; c. processing the extracted words in each of the respective data sets to determine frequency of each word occurrence; and d. sorting the processed words in order to extract a predefined number of words with highest frequency in each of the respective data set to identify Significant Multi Value Term data sets of various predefined hand-in-hand range dimensions; and another data set with words of type Single Value Term, which is the residue after Significant Multi Value Term extraction.