receiving, via a communication interface, a training dataset comprising at least an uncompressed text, a compressed text, and one or more information entities that are not mentioned in the compressed text but included in the uncompressed text;

generating, by a perturber model, a perturbed text based on inserting at least a first information entity from the one or more information entities into the compressed text;

training the perturber model based on a first training objective comparing the perturbed text generated from the compressed text and the at least first information entity, and the uncompressed text including the one or more information entities;

generating, by the trained perturber model, a perturbed summary in response to an input of a reference summary and at least a second information entity randomly selected from a corresponding source document of the reference summary;

generating, via an editor model, a predicted summary by removing information from the perturbed summary conditioned on the source document of the reference summary; and

training the editor model based on a second training objective of a cross-entropy loss between a predicted token distribution of the predicted summary conditioned on the source document and a token distribution of the reference summary.