1. A method of summarizing content using weighted Formal Concept Analysis (wFCA) comprising: (i) identifying, by a processor, a plurality of keywords in said content based on parts of speech, wherein said content comprises a plurality of sentences and said plurality of keywords; (ii) identifying, by said processor, an ambiguous keyword from said plurality of keywords, wherein said ambiguous keyword has more than one meaning; (iii) generating, by said processor, a plurality of concepts based on (a) said plurality of keywords as objects, and (b) categories associated with said plurality of keywords as attributes, and wherein said categories are obtained from a knowledge base; (iv) computing, by said processor, a score for each concept of said plurality of concepts; (v) disambiguating, by said processor, said ambiguous keyword based on said score to obtain a right meaning of said ambiguous keyword in context of a sentence in said content; (vi) identifying, by said processor, an association between said plurality of keywords and at least one sentence in said content; (vii) determining, by said processor, a weight associated with each keyword of said plurality of keywords based on a number of associations of said each keyword with said at least one sentence in said content; (viii) identifying, by said processor, a set of associated keywords that are specific to each sentence of said at least one sentence, from said plurality of keywords; (ix) determining, by said processor, a weight associated with each sentence of said at least one sentence based on a weight of each keyword of said set of associated keywords; (x) selecting at least one sentence from said plurality of sentences in said content based on said weight associated with said each sentence; and (xi) generating, by said processor, a summary of said content, wherein said summary comprises said at least one sentence selected based on said weight.
1. A method of summarizing content using weighted Formal Concept Analysis (wFCA) comprising: (i) identifying, by a processor, a plurality of keywords in said content based on parts of speech, wherein said content comprises a plurality of sentences and said plurality of keywords; (ii) identifying, by said processor, an ambiguous keyword from said plurality of keywords, wherein said ambiguous keyword has more than one meaning; (iii) generating, by said processor, a plurality of concepts based on (a) said plurality of keywords as objects, and (b) categories associated with said plurality of keywords as attributes, and wherein said categories are obtained from a knowledge base; (iv) computing, by said processor, a score for each concept of said plurality of concepts; (v) disambiguating, by said processor, said ambiguous keyword based on said score to obtain a right meaning of said ambiguous keyword in context of a sentence in said content; (vi) identifying, by said processor, an association between said plurality of keywords and at least one sentence in said content; (vii) determining, by said processor, a weight associated with each keyword of said plurality of keywords based on a number of associations of said each keyword with said at least one sentence in said content; (viii) identifying, by said processor, a set of associated keywords that are specific to each sentence of said at least one sentence, from said plurality of keywords; (ix) determining, by said processor, a weight associated with each sentence of said at least one sentence based on a weight of each keyword of said set of associated keywords; (x) selecting at least one sentence from said plurality of sentences in said content based on said weight associated with said each sentence; and (xi) generating, by said processor, a summary of said content, wherein said summary comprises said at least one sentence selected based on said weight. 7. The method of claim 1 , further comprising generating a graph to identify said association, wherein said graph comprises a plurality of nodes, wherein each node indicates a sentence in said content.