Abstract: A method for generating enriched training data for a multi-source transformer neural network for generation of a summary of one or more passages of input text comprises creating, from a plurality of input text sets, training points each comprising an input text subset of the input text set and a corresponding reference input text from the input text set, wherein the size of the input text subset is a predetermined number. Control codes are selected based on reference features corresponding to categorical labels of reference texts in the created training points. The input text is enriched with the selected control codes to generate enriched training data.

Abstract: A multi-document summarization system includes: an encoding module configured to receive multiple documents associated with a subject and to, using a first model, generate vector representations for sentences, respectively, of the documents; a grouping module configured to group first and second ones of the sentences associated with first and second aspects into first and second groups, respectively; a group representation module configured to generate a first vector representation based on the first ones of the sentences and a second vector representation based on the second ones of the sentences; a summary module configured to: using a second model: generate a first sentence regarding the first aspect based on the first vector representation; and generate a second sentence regarding the second aspect based on the second vector representation; and store a summary including the first and second sentences in memory in association with the subject.

Abstract: A method for generating enriched training data for a multi-source transformer neural network for generation of a summary of one or more passages of input text comprises creating, from a plurality of input text sets, training points each comprising an input text subset of the input text set and a corresponding reference input text from the input text set, wherein the size of the input text subset is a predetermined number. Control codes are selected based on reference features corresponding to categorical labels of reference texts in the created training points. The input text is enriched with the selected control codes to generate enriched training data.

Abstract: A multi-document summarization system includes: an encoding module configured to receive multiple documents associated with a subject and to, using a first model, generate vector representations for sentences, respectively, of the documents; a grouping module configured to group first and second ones of the sentences associated with first and second aspects into first and second groups, respectively; a group representation module configured to generate a first vector representation based on the first ones of the sentences and a second vector representation based on the second ones of the sentences; a summary module configured to: using a second model: generate a first sentence regarding the first aspect based on the first vector representation; and generate a second sentence regarding the second aspect based on the second vector representation; and store a summary including the first and second sentences in memory in association with the subject.