1. A computer-implemented method for summarizing a concept, the method comprising: receiving, from a user, a query corresponding to the concept; identifying a source based on the query; collecting, from the source, a plurality of images along with descriptive information corresponding thereto; forming feature vectors with processed descriptive information by processing the plurality of images and the descriptive information; assigning each of the plurality of images with one or more topic distribution values corresponding to one or more topics, the one or more topics corresponding to the feature vectors with the processed descriptive information; receiving, from the user, a summary size that represents a number of images to be a size of a sparse set of images; determining the sparse set of images from the plurality of images to summarize the concept, wherein a size of the sparse set of images is the summary size and the sparse set of images being determined based on the feature vectors and the assigned topic distribution values, wherein the sparse set of images is limited to the summary size; and regularizing one or more distribution constraints for building a target summary from the summarized concept, the one or more distribution constraints being regularized by utilizing a regularization term to minimize divergence between the summarized concept and the target summary, wherein the regularization term is received from the user; providing the sparse set of images to the user responsive to the query from the user.
1. A computer-implemented method for summarizing a concept, the method comprising: receiving, from a user, a query corresponding to the concept; identifying a source based on the query; collecting, from the source, a plurality of images along with descriptive information corresponding thereto; forming feature vectors with processed descriptive information by processing the plurality of images and the descriptive information; assigning each of the plurality of images with one or more topic distribution values corresponding to one or more topics, the one or more topics corresponding to the feature vectors with the processed descriptive information; receiving, from the user, a summary size that represents a number of images to be a size of a sparse set of images; determining the sparse set of images from the plurality of images to summarize the concept, wherein a size of the sparse set of images is the summary size and the sparse set of images being determined based on the feature vectors and the assigned topic distribution values, wherein the sparse set of images is limited to the summary size; and regularizing one or more distribution constraints for building a target summary from the summarized concept, the one or more distribution constraints being regularized by utilizing a regularization term to minimize divergence between the summarized concept and the target summary, wherein the regularization term is received from the user; providing the sparse set of images to the user responsive to the query from the user. 2. The method of claim 1 further comprising: identifying an adapter from a plurality of adapters depending on the identified source, wherein the plurality of adapters each correspond to a different source.