Abstract: Systems and methods are disclosed processing a document. A method includes receiving, by a processor coupled to a memory, layout information for two or more layouts of a document, each layout of the two or more layouts having a layout bounding box. The method includes identifying, by the processor, one or more areas of overlap between the layout bounding boxes of each layout, respectively. The method includes identifying, by the processor, content associated with each area of overlap. Further, the method may include use the layout information, the areas of overlap, and the identified content associated with each area of overlap as inputs to a machine learning model. Further, the method may include receiving, by the processor from the machine-learning model, a layout bounding box configuration for one or more of the layouts of the document. Further, the method may include apply the layout bounding box configuration to the document.

Abstract: A computer-implemented method for identifying a problem list section from an electronic document includes receiving, by one or more processors, the electronic document, generating, by the one or more processors and based on applying an optical character recognition algorithm to the electronic document, unstructured text, and identifying, by the one or more processors, one or more problem list words in the unstructured text, the one or more problem list words belonging in a dataset for identifying a presence of a problem list section. The method also includes associating, by the one or more processors, a portion of the unstructured text that corresponds to the one or more problem list words in the unstructured text with the problem list section and outputting, by the one or more processors, at least a portion of the problem list section.

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.

Abstract: There is a need for more effective and efficient predictive natural language summarization. This need is addressed by applying hybrid extractive and abstractive summarization techniques in a unique processing pipeline to generate a cohesive and comprehensive summary of a multi-party interaction.

Abstract: There is a need for more effective and efficient predictive data analysis solutions and/or more effective and efficient solutions for generating an emotional sentiment score without the use of labelled data. In one example, embodiments comprise receiving an input text sequence, generating an intermediate emotional sentiment score object based at least in part on the input text sequence and by utilizing an emotional sentiment machine learning model, generating an overall emotional sentiment score based at least in part on the intermediate sentiment score object and by utilizing an emotional sentiment score transformation object, and performing one or more prediction-based actions based at least in part on the overall emotional sentiment score.

Abstract: Methods, apparatuses, systems, computing entities, and/or the like are provided. An example method may include retrieving a scheme data object; generating a tabular data object; generating a graph data object including a plurality of nodes and a plurality of edges; calculating, based on the graph data object, a plurality of node attributes for the plurality of nodes and a plurality of edge attributes for the plurality of edges; identifying at least one irregular member node from the plurality of member nodes; identifying at least one irregular referring provider node from the plurality of referring provider nodes; identifying at least one irregular servicing provider node from the plurality of servicing provider nodes based at least in part on the at least one irregular member node and the at least one irregular referring provider node; and performing one or more responsive actions.