Abstract: An example system includes a processor to receive concepts extracted from a result set corresponding to a query and result associations for each extracted concept. The processor is to build a graph based on the extracted concepts, wherein the graph comprises a number of nodes representing the extracted concepts and weighted edges representing similarity between concepts extracted from shared results. The processor is to partition the graph into subgraphs with vertices corresponding to candidate facets for vertices having higher sums of weighted edges. The processor is to rank the candidate facets. The processor is to select higher ranked candidate facets to use as facets. The processor is to output facets with a result set in response to the query.

Abstract: A method including: Obtaining multiple conversation texts, one text per conversation, wherein each of the multiple conversation texts comprises: multiple messages authored by multiple parties, and a reference to an electronic document that provides resolution of a problem that is common to all the conversations. Calculating an importance score for each of the multiple messages of all the conversation texts. Clustering the multiple messages of all the conversation texts into multiple bins. Calculating an aggregated importance score for each of the multiple bins, based on the importance scores of the messages contained in the respective bin. Enriching (a) the electronic document, or (b) a record of the electronic document in an index of electronic documents, with at least some of the multiple bins and their aggregated importance scores, wherein the at least some of the multiple bins are added as fields to the electronic document or to the record.

Abstract: An example system includes a processor to receive concepts extracted from a result set corresponding to a query and result associations for each extracted concept. The processor is to build a graph based on the extracted concepts, wherein the graph comprises a number of nodes representing the extracted concepts and weighted edges representing similarity between concepts extracted from shared results. The processor is to partition the graph into subgraphs with vertices corresponding to candidate facets for vertices having higher sums of weighted edges. The processor is to rank the candidate facets. The processor is to select higher ranked candidate facets to use as facets. The processor is to output facets with a result set in response to the query.

Abstract: A method including: Obtaining multiple conversation texts, one text per conversation, wherein each of the multiple conversation texts comprises: multiple messages authored by multiple parties, and a reference to an electronic document that provides resolution of a problem that is common to all the conversations. Calculating an importance score for each of the multiple messages of all the conversation texts. Clustering the multiple messages of all the conversation texts into multiple bins. Calculating an aggregated importance score for each of the multiple bins, based on the importance scores of the messages contained in the respective bin. Enriching (a) the electronic document, or (b) a record of the electronic document in an index of electronic documents, with at least some of the multiple bins and their aggregated importance scores, wherein the at least some of the multiple bins are added as fields to the electronic document or to the record.