Machine-learning based irrelevant sentence classifier

Abstract

There is a need for more effective, efficient, and accurate computer text comprehension. This need is addressed by applying unique text processing techniques to identify and remove irrelevant sentences from a narrative. The text processing techniques include a machine-learning based model that is trained using automatically generated training data that is tailored to a particular circumstance. A method for machine narrative comprehension includes receiving a narrative data object comprising one or more sentences; determining, using a machine-learning based irrelevant classifier model, a relevance of at least one of the one or more sentences; responsive to a determination that at least one sentence is irrelevant, generating a pertinent summary by removing the at least one sentence from the narrative; and generating, based at least in part on the pertinent summary, an output indicia data object for the narrative data object.