Abstract: In a general purpose computer, a method of extracting snippets includes receiving textual content and a plurality of available topics, dividing the textual content into a plurality of snippets, converting each of the snippets to a vector, determining a distance between coadjacent snippets of the plurality of snippets in the textual content, determining an update to the plurality of snippets by merging each of the pairs of coadjacent snippets having a respective distance less than a second threshold, wherein an updated plurality of snippets includes merged snippets, generating a plurality of clusters from the updated plurality of snippets, each cluster associated with one topic selected from the plurality of available topics, and generating, for each of the snippets of the updated plurality of snippets, an affinity score for each of the clusters, each affinity score measuring an assignment strength of a given snippet to a given cluster, and a dominant topic among the at least one identified topic.

Abstract: In a general purpose computer, a method of extracting snippets includes receiving textual content and a plurality of available topics, dividing the textual content into a plurality of snippets, converting each of the snippets to a vector, determining a distance between coadjacent snippets of the plurality of snippets in the textual content, determining an update to the plurality of snippets by merging each of the pairs of coadjacent snippets having a respective distance less than a second threshold, wherein an updated plurality of snippets includes merged snippets, generating a plurality of clusters from the updated plurality of snippets, each cluster associated with one topic selected from the plurality of available topics, and generating, for each of the snippets of the updated plurality of snippets, an affinity score for each of the clusters, each affinity score measuring an assignment strength of a given snippet to a given cluster, and a dominant topic among the at least one identified topic.