1. A multilingual method for summarizing an article, which comprises: an offline stage which comprises: (a.) predefining a set of metrics; (b.) providing a collection of documents, and providing one or more of expert summaries for each article; (c.) indexing the sentences and words within each article; (d.) subjecting, using at least one computer processor, each sentence in said articles serially to the entire set of metrics, thereby obtaining for each sentence a plurality of sentence metrics values, each relating to one of said metrics respectively; (e.) guessing a population of u normalized weights vectors; (f.) for a selected weights vector u i in said population: (f.1.) for each sentence, calculating a sentence combined value, said combined value being a linear combination of the weights vector and said sentence metrics values; (f.2.) for each document ranking the sentences according to their combined values; (f.3.) for each document selecting a group of sentences having highest combined values; (f.4.) for each document, comparing said selected group with the one or more expert summaries, and obtaining a quality score for each article, selected group, and corresponding weights vector; (f.5.) repeating steps f1 to f4 for all the weights vectors in u; (g.) based on said quality scores, calculating a total score, and checking for convergence of a total of said quality scores in respect to previous iterations; (h.) upon convergence of said total quality scores, selecting a best weights vector which provides highest quality scores, and terminating the process; (i.) otherwise, if no convergence has yet obtained, selecting a group a of weights vectors out of population u that have provided highest quality scores, and by means of a genetic algorithm generating a new population u′ of weights vectors, and repeating steps f to h with the population u′ of weights vectors until convergence; and a real time stage which comprises: (j.) indexing sentences, and words within the document which needs summarization; (k.) calculating each of said predefined metrics of step a with respect to each of the sentences in the document to obtain sentence metric values; (l.) separately for each sentence, subjecting the sentence matrix values to the best weights vector as selected in step h of the offline stage, and summing up all weighted values to obtain a single combined value for each sentence; (m.) ranking the sentences according to their combined values to form a ranked list, and extracting a predetermined number of sentences from a top of the ranked list of sentences; and (n.) combining said extracted sentences thereby forming the document summary.
1. A multilingual method for summarizing an article, which comprises: an offline stage which comprises: (a.) predefining a set of metrics; (b.) providing a collection of documents, and providing one or more of expert summaries for each article; (c.) indexing the sentences and words within each article; (d.) subjecting, using at least one computer processor, each sentence in said articles serially to the entire set of metrics, thereby obtaining for each sentence a plurality of sentence metrics values, each relating to one of said metrics respectively; (e.) guessing a population of u normalized weights vectors; (f.) for a selected weights vector u i in said population: (f.1.) for each sentence, calculating a sentence combined value, said combined value being a linear combination of the weights vector and said sentence metrics values; (f.2.) for each document ranking the sentences according to their combined values; (f.3.) for each document selecting a group of sentences having highest combined values; (f.4.) for each document, comparing said selected group with the one or more expert summaries, and obtaining a quality score for each article, selected group, and corresponding weights vector; (f.5.) repeating steps f1 to f4 for all the weights vectors in u; (g.) based on said quality scores, calculating a total score, and checking for convergence of a total of said quality scores in respect to previous iterations; (h.) upon convergence of said total quality scores, selecting a best weights vector which provides highest quality scores, and terminating the process; (i.) otherwise, if no convergence has yet obtained, selecting a group a of weights vectors out of population u that have provided highest quality scores, and by means of a genetic algorithm generating a new population u′ of weights vectors, and repeating steps f to h with the population u′ of weights vectors until convergence; and a real time stage which comprises: (j.) indexing sentences, and words within the document which needs summarization; (k.) calculating each of said predefined metrics of step a with respect to each of the sentences in the document to obtain sentence metric values; (l.) separately for each sentence, subjecting the sentence matrix values to the best weights vector as selected in step h of the offline stage, and summing up all weighted values to obtain a single combined value for each sentence; (m.) ranking the sentences according to their combined values to form a ranked list, and extracting a predetermined number of sentences from a top of the ranked list of sentences; and (n.) combining said extracted sentences thereby forming the document summary. 2. The method according to claim 1 , wherein the offline stage is performed only once, while the best weights vector which is determined as a result of the offline stage is used thereafter in any performance of the real time stage.