A computer-implemented method for summarizing an electronic document, the method comprising: generating a feature vector for for the electronic document, wherein the feature vector comprises a plurality of features of the electronic document; assigning a weight to each of the plurality of features, wherein the respective weight is automatically learned, and wherein the assigning the respective weight comprises, for a given feature of the plurality of features (i) boosting the respective weight when the given feature occurs with at least a defined frequency in a set of training examples comprising documents that are summarizable, and (ii) lowering the respective weight when the given feature occurs with at least a defined frequency in a set of training examples comprising documents that are not summarizable; assigning a summarizability score to to the electronic document in accordance with the weight assigned to each of the plurality of features, wherein the summarizability score indicates whether the electronic document is summarizable; and if the electronic document is summarizable: splitting the electronic document into a plurality of terms, wherein each of the plurality of terms is associated with a respective length, a respective informativeness score, and a respective coherence score; automatically selecting a subset of the plurality of terms, such that an aggregate informativeness score of the subset is maximized while an aggregate length of the subset is less than or equal to a maximum length; and arranging the subset as a summary of the electronic document. generating a feature vector for for the electronic document, wherein the feature vector comprises a plurality of features of the electronic document; assigning a weight to each of the plurality of features, wherein the respective weight is automatically learned, and wherein the assigning the respective weight comprises, for a given feature of the plurality of features (i) boosting the respective weight when the given feature occurs with at least a defined frequency in a set of training examples comprising documents that are summarizable, and (ii) lowering the respective weight when the given feature occurs with at least a defined frequency in a set of training examples comprising documents that are not summarizable; assigning a summarizability score to to the electronic document in accordance with the weight assigned to each of the plurality of features, wherein the summarizability score indicates whether the electronic document is summarizable; and if the electronic document is summarizable: splitting the electronic document into a plurality of terms, wherein each of the plurality of terms is associated with a respective length, a respective informativeness score, and a respective coherence score; automatically selecting a subset of the plurality of terms, such that an aggregate informativeness score of the subset is maximized while an aggregate length of the subset is less than or equal to a maximum length; and arranging the subset as a summary of the electronic document. splitting the electronic document into a plurality of terms, wherein each of the plurality of terms is associated with a respective length, a respective informativeness score, and a respective coherence score; automatically selecting a subset of the plurality of terms, such that an aggregate informativeness score of the subset is maximized while an aggregate length of the subset is less than or equal to a maximum length; and arranging the subset as a summary of the electronic document.