13. A computer implemented method for determining a compression of a sentence, comprising: identifying, by one or more computing systems, a compression rate; determining, by one or more of the computing systems, a transform of a given sentence having a plurality of sentence terms, the transform including a plurality of nodes and a plurality of edges, each of the nodes including a root node or one or more of the sentence terms, and each of the edges defining a hierarchical link between two of the nodes; determining by a trained compression computing system of the computing systems, edge weights for each of the edges, wherein determining the edge weight for a given edge of the edges includes: determining, by the trained compression computing system, features of the given edge; mapping, by the trained compression computing system, each of the features of the given edge to weights of corresponding features of a set of features, wherein the weights of the set of features are iteratively learned by the trained compression system based on updating the weights during a plurality of iterations of learning based on a corpus of sentences and associated sentence compressions; and determining, by the trained compression computing system, m the given edge weight based on the weights of the corresponding features of the set of features; determining, by one or more of the computing systems, a subtree of the transform that: is a valid syntactic tree, that satisfies the compression rate, and whose edges provide an optimal total of the edge weights as compared to any other valid syntactic trees that satisfy the compression rate.
13. A computer implemented method for determining a compression of a sentence, comprising: identifying, by one or more computing systems, a compression rate; determining, by one or more of the computing systems, a transform of a given sentence having a plurality of sentence terms, the transform including a plurality of nodes and a plurality of edges, each of the nodes including a root node or one or more of the sentence terms, and each of the edges defining a hierarchical link between two of the nodes; determining by a trained compression computing system of the computing systems, edge weights for each of the edges, wherein determining the edge weight for a given edge of the edges includes: determining, by the trained compression computing system, features of the given edge; mapping, by the trained compression computing system, each of the features of the given edge to weights of corresponding features of a set of features, wherein the weights of the set of features are iteratively learned by the trained compression system based on updating the weights during a plurality of iterations of learning based on a corpus of sentences and associated sentence compressions; and determining, by the trained compression computing system, m the given edge weight based on the weights of the corresponding features of the set of features; determining, by one or more of the computing systems, a subtree of the transform that: is a valid syntactic tree, that satisfies the compression rate, and whose edges provide an optimal total of the edge weights as compared to any other valid syntactic trees that satisfy the compression rate. 19. The method of claim 13 , wherein determining the transform of the given sentence includes: parsing the sentence with a dependency parser to generate a source dependency tree having a plurality of part of speech labels connecting the sentence terms; and transforming the source dependency tree to create the transform of the given sentence.