Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for using artificial intelligence to facilitate electronic communication between participants. For example, in one or more embodiments, the disclosed systems use artificial intelligence models to generate and provide a communication summary describing the contents of a communication. To illustrate, the disclosed systems can generate a transcript for a communication and use various computer-implemented models, such as a large language model or a set of categorization models to generate a communication summary for the transcript. In some instances, the disclosed systems use a summary template with the categorization models to incorporate certain pre-configured types of information into the communication summary.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for using artificial intelligence to facilitate electronic communication between participants. For example, in one or more embodiments, the disclosed systems perform operations to generate and provide relevant notifications to a client device participating in a communication as the communication is ongoing. In particular, during the communication, the disclosed systems can utilize one or more artificial intelligence models to analyze a communication stream, generate a transcript, enrich the transcript with contextual information, and/or retrieve digital content for use in generating and providing notifications. The disclosed systems can provide the notifications to the participating client device during the communication to assist with the discussion.

Abstract: Implementations analyze transaction data and objectively capture pre-identified desired information about the analyzed transaction data in a consistently organized manner. An example system includes a user interface that enables a user to provide static portions and dynamic portions of a template. The dynamic portions identify variables that are replaced with either data extracted from the transaction or text based on the output of classifiers applied to the transaction. An example method includes applying classifiers to scoring units of a transaction to generate classifier tags for the scoring units and generating a narrative by replacing variables in an automated narrative template with text based on at least some of the classifier tags.

Abstract: Implementations analyze transaction data and objectively capture pre-identified desired information about the analyzed transaction data in a consistently organized manner. An example system includes a user interface that enables a user to provide static portions and dynamic portions of a template. The dynamic portions identify variables that are replaced with either data extracted from the transaction or text based on the output of classifiers applied to the transaction. An example method includes applying classifiers to scoring units of a transaction to generate classifier tags for the scoring units and generating a narrative by replacing variables in an automated narrative template with text based on at least some of the classifier tags.

Abstract: Implementations analyze transaction data and objectively capture pre-identified desired information about the analyzed transaction data in a consistently organized manner. An example system includes a user interface that enables a user to provide static portions and dynamic portions of a template. The dynamic portions identify variables that are replaced with either data extracted from the transaction or text based on the output of classifiers applied to the transaction. An example method includes applying classifiers to scoring units of a transaction to generate classifier tags for the scoring units and generating a narrative by replacing variables in an automated narrative template with text based on at least some of the classifier tags.