Abstract: Embodiments are directed to summarizing conversational speech. Conversation segments may be provided based on a conversation stream and segmentation models. Summarization models may be determined based on characteristics of the conversation segments. Summarization information may be generated for each of the conversation segments based on the summarization models such that the summarization information includes a text-based summarization of the conversation segment. Summarization profiles may be generated for the conversation segments based on the summarization information such that each summarization profile is associated with quality scores. Summarization models may be modified based on the summarization profiles and the associated quality scores such that the summarization profiles are updated based on the modified summarization models. Modified summarization models and the updated summarization profiles may be employed to provide reports to a user.

Abstract: Embodiments are directed to summarizing conversational speech. Conversation segments may be provided based on a conversation stream and segmentation models. Summarization models may be determined based on characteristics of the conversation segments. Summarization information may be generated for each of the conversation segments based on the summarization models such that the summarization information includes a text-based summarization of the conversation segment. Summarization profiles may be generated for the conversation segments based on the summarization information such that each summarization profile is associated with quality scores. Summarization models may be modified based on the summarization profiles and the associated quality scores such that the summarization profiles are updated based on the modified summarization models. Modified summarization models and the updated summarization profiles may be employed to provide reports to a user.

Abstract: Embodiments are directed to organizing conversation information. Two or more machine learning (ML) models and a plurality of sentences provided from a conversation may be employed to generate insight scores for each sentence such that each insight score correlates to a probability that its sentence includes one or more of an action or a question. In response to one or more sentences having insight scores that exceed a threshold value an information score and a definiteness score may be determined for the one or more sentences. And one or more insights associated with the conversation may be generated based on the one or more sentences. A report may be generated that associates the one or more insights with one or more portions of the conversation that include the one or more sentences that are associated with the insights.

Abstract: Embodiments are directed to organizing conversation information. A tracker vocabulary may be provided to a universal model to predict a generalized vocabulary associated with the tracker vocabulary. A tracker model may be generated based on the portions of the universal model activated by the tracker vocabulary such that a remainder of the universal model may be excluded from the tracker model. Portions of a conversation stream may be provided to the tracker model. A match score may be generated based on the track model and the portions of the conversation stream such that the match score predicts if the portions of the conversation stream may be in the generalized vocabulary predicted for the tracker vocabulary. Tracker metrics may be collected based on the portions of the conversation and the match scores such that the tracker metrics may be included in reports or notifications.

Abstract: Embodiments are directed to organizing conversations. Words may be provided from a conversation stream. Each word may be mapped to a graph model based on characteristics of each word. The graph model may be partitioned based on one or more attributes of a nodes and edges included in the graph model such that nodes associated with relationship strength that exceeds a threshold value may be grouped together. Sentence models may be generated based on sentences included in the conversation stream. Combined models may be generated based on the sentence models and the graph such that each sentence model may be associated with one or more partitions of the graph model. A conversation digest may be generated based on the combined model such that the conversation digest identifies one or more dominant portions of the conversation that include key subject matter.

Abstract: Embodiments are directed to organizing conversations. Words may be provided from a conversation stream. Each word may be mapped to a graph model based on characteristics of each word. The graph model may be partitioned based on one or more attributes of a nodes and edges included in the graph model such that nodes associated with relationship strength that exceeds a threshold value may be grouped together. Sentence models may be generated based on sentences included in the conversation stream. Combined models may be generated based on the sentence models and the graph such that each sentence model may be associated with one or more partitions of the graph model. A conversation digest may be generated based on the combined model such that the conversation digest identifies one or more dominant portions of the conversation that include key subject matter.

Abstract: Embodiments are directed to organizing conversation information. Two or more machine learning (ML) models and a plurality of sentences provided from a conversation may be employed to generate insight scores for each sentence such that each insight score correlates to a probability that its sentence includes one or more of an action or a question. In response to one or more sentences having insight scores that exceed a threshold value an information score and a definiteness score may be determined for the one or more sentences. And one or more insights associated with the conversation may be generated based on the one or more sentences. A report may be generated that associates the one or more insights with one or more portions of the conversation that include the one or more sentences that are associated with the insights.

Abstract: Embodiments are directed to organizing conversations. Words may be provided from a conversation stream. Each word may be mapped to a graph model based on characteristics of each word. The graph model may be partitioned based on one or more attributes of a nodes and edges included in the graph model such that nodes associated with relationship strength that exceeds a threshold value may be grouped together. Sentence models may be generated based on sentences included in the conversation stream. Combined models may be generated based on the sentence models and the graph such that each sentence model may be associated with one or more partitions of the graph model. A conversation digest may be generated based on the combined model such that the conversation digest identifies one or more dominant portions of the conversation that include key subject matter.

Abstract: Embodiments are directed to organizing conversations. Words may be provided from a conversation stream. Each word may be mapped to a graph model based on characteristics of each word. The graph model may be partitioned based on one or more attributes of a nodes and edges included in the graph model such that nodes associated with relationship strength that exceeds a threshold value may be grouped together. Sentence models may be generated based on sentences included in the conversation stream. Combined models may be generated based on the sentence models and the graph such that each sentence model may be associated with one or more partitions of the graph model. A conversation digest may be generated based on the combined model such that the conversation digest identifies one or more dominant portions of the conversation that include key subject matter.

Abstract: Embodiments are directed to organizing conversation information. A tracker vocabulary may be provided to a universal model to predict a generalized vocabulary associated with the tracker vocabulary. A tracker model may be generated based on the portions of the universal model activated by the tracker vocabulary such that a remainder of the universal model may be excluded from the tracker model. Portions of a conversation stream may be provided to the tracker model. A match score may be generated based on the track model and the portions of the conversation stream such that the match score predicts if the portions of the conversation stream may be in the generalized vocabulary predicted for the tracker vocabulary. Tracker metrics may be collected based on the portions of the conversation and the match scores such that the tracker metrics may be included in reports or notifications.

Abstract: Embodiments are directed to organizing conversation information. Two or more machine learning (ML) models and a plurality of sentences provided from a conversation may be employed to generate insight scores for each sentence such that each insight score correlates to a probability that its sentence includes one or more of an action or a question. In response to one or more sentences having insight scores that exceed a threshold value an information score and a definiteness score may be determined for the one or more sentences. And one or more insights associated with the conversation may be generated based on the one or more sentences. A report may be generated that associates the one or more insights with one or more portions of the conversation that include the one or more sentences that are associated with the insights.

Abstract: Embodiments are directed to organizing conversation information. Two or more machine learning (ML) models and a plurality of sentences provided from a conversation may be employed to generate insight scores for each sentence such that each insight score correlates to a probability that its sentence includes one or more of an action or a question. In response to one or more sentences having insight scores that exceed a threshold value an information score and a definiteness score may be determined for the one or more sentences. And one or more insights associated with the conversation may be generated based on the one or more sentences. A report may be generated that associates the one or more insights with one or more portions of the conversation that include the one or more sentences that are associated with the insights.

Abstract: Embodiments are directed to organizing conversations. Words may be provided from a conversation stream. Each word may be mapped to a graph model based on characteristics of each word. The graph model may be partitioned based on one or more attributes of a nodes and edges included in the graph model such that nodes associated with relationship strength that exceeds a threshold value may be grouped together. Sentence models may be generated based on sentences included in the conversation stream. Combined models may be generated based on the sentence models and the graph such that each sentence model may be associated with one or more partitions of the graph model. A conversation digest may be generated based on the combined model such that the conversation digest identifies one or more dominant portions of the conversation that include key subject matter.

Abstract: A system, method, and computer program product are provided for performing bot engine abstraction. In use, a message is received at a bot engine from a messaging channel, utilizing a first abstraction layer. Additionally, one or more external components are accessed by the bot engine, utilizing a second abstraction layer, to create a response to the message. Further, the response is sent from the bot engine to the messaging channel, utilizing the first abstraction layer.