45. The computer program as recited in claim 32 , wherein the one or more discourse comprehension features are derived by the steps of: reading a sentence from the electronic document; extracting noun phrases from the sentence; constructing a graph in a working memory with nodes corresponding to the noun phrases; adding nodes to the graph for all phrases that are in a short-term memory; connecting all nodes with edges weighted by a semantic relatedness measure derived using a semantic memory; decreasing the weights on incoming and outgoing edges of nodes coming from the short-term memory by a decay factor proportional to the number of sentences since the phrase was last seen; ranking the nodes in the graph using a graph centrality measure; selecting a predetermined number of the highest-ranked nodes and placing their associated phrases in short-term memory; saving parameters related to a phrase ranking using the centrality measure and a number of iterations that the phrase remains in the short-term memory; repeating the above steps of reading, extracting, constructing, adding, connecting, decreasing, ranking, selecting and saving until all sentences have been read from the electronic document; and calculating the one or more discourse comprehension features using the saved parameters.
45. The computer program as recited in claim 32 , wherein the one or more discourse comprehension features are derived by the steps of: reading a sentence from the electronic document; extracting noun phrases from the sentence; constructing a graph in a working memory with nodes corresponding to the noun phrases; adding nodes to the graph for all phrases that are in a short-term memory; connecting all nodes with edges weighted by a semantic relatedness measure derived using a semantic memory; decreasing the weights on incoming and outgoing edges of nodes coming from the short-term memory by a decay factor proportional to the number of sentences since the phrase was last seen; ranking the nodes in the graph using a graph centrality measure; selecting a predetermined number of the highest-ranked nodes and placing their associated phrases in short-term memory; saving parameters related to a phrase ranking using the centrality measure and a number of iterations that the phrase remains in the short-term memory; repeating the above steps of reading, extracting, constructing, adding, connecting, decreasing, ranking, selecting and saving until all sentences have been read from the electronic document; and calculating the one or more discourse comprehension features using the saved parameters. 46. The computer program as recited in claim 45 , wherein the semantic relatedness measure comprises a weighted combination of a variant of latent semantic analysis trained on books and articles and a pointwise mutual information measure.