9. A server having one or more processors configured to perform operations comprising: obtaining a training corpus including pairs of (i) documents and (ii) abstracts, each abstract representing a summary of a corresponding document; obtaining part-of-speech (POS) tags and dependency parses for each abstract and each corresponding document; identifying a set of entity mentions in each abstract and each corresponding document based on their respective POS tags and dependency parses; clustering the sets of entity mentions referring to a same underlying entity to obtain clusters for each document and each corresponding abstract; aligning specific abstract entity mentions to corresponding document entity mentions to obtain a set of aligned abstract and document entities; labeling the set of aligned entities as salient and unaligned entities as non-salient to generate a labeled corpus; training features of a classifier using the labeled corpus to obtain a trained classifier and generating a model including the trained classifier, wherein the features include count features indicative of at least one of entity mentions and entity head word mentions, and wherein the training and generating further comprises bucketing the count features by applying a function to the count features, wherein the function is: 
 ƒ( x )=round(log( k ( x+ 1))), where x represents a particular count feature and k controls the number of buckets; applying the model to a collection of documents to obtain the salience estimates, the collection of documents representing a web browsing history of a user of a computing device; utilizing the salience estimates to obtain a prediction of an advertisement that should be displayed to the user; and outputting, to the computing device via a network, the prediction, wherein receipt of the prediction causes the computing device to display the advertisement to the user.
9. A server having one or more processors configured to perform operations comprising: obtaining a training corpus including pairs of (i) documents and (ii) abstracts, each abstract representing a summary of a corresponding document; obtaining part-of-speech (POS) tags and dependency parses for each abstract and each corresponding document; identifying a set of entity mentions in each abstract and each corresponding document based on their respective POS tags and dependency parses; clustering the sets of entity mentions referring to a same underlying entity to obtain clusters for each document and each corresponding abstract; aligning specific abstract entity mentions to corresponding document entity mentions to obtain a set of aligned abstract and document entities; labeling the set of aligned entities as salient and unaligned entities as non-salient to generate a labeled corpus; training features of a classifier using the labeled corpus to obtain a trained classifier and generating a model including the trained classifier, wherein the features include count features indicative of at least one of entity mentions and entity head word mentions, and wherein the training and generating further comprises bucketing the count features by applying a function to the count features, wherein the function is: 
 ƒ( x )=round(log( k ( x+ 1))), where x represents a particular count feature and k controls the number of buckets; applying the model to a collection of documents to obtain the salience estimates, the collection of documents representing a web browsing history of a user of a computing device; utilizing the salience estimates to obtain a prediction of an advertisement that should be displayed to the user; and outputting, to the computing device via a network, the prediction, wherein receipt of the prediction causes the computing device to display the advertisement to the user. 10. The server of claim 9 , wherein the identification of specific abstract entities and specific document entities is limited to those having at least one proper name mention, and wherein aligning a specific abstract entity to a specific document entity requires that one of the abstract entity mentions shares a syntactic head token with one of the document entity mentions.