7. A system for determining a set of keywords associated with a document for use as suggested search terms, comprising: a processor; and a memory containing a program, which, when executed by the processor, is configured to perform a process configured to: receive a categorization ranking for a document received from a device via a network for each category of a plurality of categories of a taxonomy, and determine a set of categories of the taxonomy having highest categorization rankings of the plurality of categories for the received document; combine together the documents representing the categories of the determined set of categories into a cumulative representative text that includes a plurality of terms, the plurality of terms including at least one term that is not included in the received document; determine a term corpus importance score for each term in the cumulative representative text for each category of the set of categories based on a term frequency and an inverse category frequency; determine a cumulative term corpus importance score for each term in the cumulative representative text by combining the term corpus importance scores generated for each term for categories of the set of categories, the cumulative term corpus importance score for a particular term indicating an importance of the particular term in a context of the cumulative representative text; select a set of terms of the cumulative representative text having highest cumulative term corpus importance scores of the plurality of terms as the keywords; and provide one or more of the keywords as one or more suggested search terms to the device via the network.
7. A system for determining a set of keywords associated with a document for use as suggested search terms, comprising: a processor; and a memory containing a program, which, when executed by the processor, is configured to perform a process configured to: receive a categorization ranking for a document received from a device via a network for each category of a plurality of categories of a taxonomy, and determine a set of categories of the taxonomy having highest categorization rankings of the plurality of categories for the received document; combine together the documents representing the categories of the determined set of categories into a cumulative representative text that includes a plurality of terms, the plurality of terms including at least one term that is not included in the received document; determine a term corpus importance score for each term in the cumulative representative text for each category of the set of categories based on a term frequency and an inverse category frequency; determine a cumulative term corpus importance score for each term in the cumulative representative text by combining the term corpus importance scores generated for each term for categories of the set of categories, the cumulative term corpus importance score for a particular term indicating an importance of the particular term in a context of the cumulative representative text; select a set of terms of the cumulative representative text having highest cumulative term corpus importance scores of the plurality of terms as the keywords; and provide one or more of the keywords as one or more suggested search terms to the device via the network. 9. The system of claim 7 , wherein the process is further configured to apply a scaling factor to words of a first category of the set of categories having a categorization ranking that is greater than at least one other category of the set of categories to increase a likelihood that the words of the first category appear in the cumulative representative text and/or an importance of the words.