Systems and methods for text summarization

A system for text summarization, the system comprising:

• a communication interface that receives a training dataset comprising at least an uncompressed text, a compressed text, and one or more information entities accompanying the compressed text;
• a memory storing a plurality of processor-executable instructions;
• and
• a processor executing the instructions to perform operations comprising:
• generating, by a perturber model, a perturbed text with the one or more information entities being inserted into the compressed text;
• training the perturber model based on a first training objective comparing the perturbed text and the uncompressed text;
• generating, by the trained perturber model, a perturbed summary in response to an input of a reference summary;
• generating, via an editor model, a predicted summary by removing information from the perturbed summary conditioned on a source document of the reference summary;
• and
• training the editor model based on a second training objective comparing the predicted summary and the reference summary, wherein the training of the editor model comprises computing 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.

A non-transitory processor-readable storage medium storing a plurality of processor-executable instructions for code program synthesis for a target problem, the instructions being executed by one or more hardware processors to perform operations comprising:

• receiving, via a communication interface, a training dataset comprising at least an uncompressed text, a compressed text, and one or more information entities accompanying the compressed text;
• generating, by a perturber model, a perturbed text with the one or more information entities being inserted into the compressed text;
• training the perturber model based on a first training objective comparing the perturbed text and the uncompressed text;
• generating, by the trained perturber model, a perturbed summary in response to an input of a reference summary;
• generating, via an editor model, a predicted summary by removing information from the perturbed summary conditioned on a source document of the reference summary;
• and
• training the editor model based on a second training objective comparing the predicted summary and the reference summary, wherein the training of the editor model comprises computing 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.