Atlassian Corporation Pty Ltd.

Atlassian US, Inc.

Aneesh Kundu

Karthik Muralidharan

Sri Vardhamanan A

Ayushi Gupta

Vasu Gupta

Utkarsh Tyagi

Methods, apparatuses, or computer program products provide for enabling generation of abstractive context summaries for multi-party communication channels. An abstractive context summary scheduling interface associated with a selected multi-party communication channel may be caused to be rendered to a client computing device associated with a member profile identifier. A summary generation parameter set may be received in response to user engagement with the abstractive context summary scheduling interface. A plurality of communication data objects from the selected multi-party communication channel may be extracted based on the summary generation parameter set. An abstractive context summary for the selected multi-party communication channel may be generated based on the plurality of communication data objects and utilizing a text summarization machine learning model. The abstractive context summary may be caused to be rendered for display on the client computing device associated with the member profile identifier.

Apparatuses, methods, and computer program products for generating an abstractive context summary scheduling interface configured for scheduling and outputting abstractive context summaries for multi-party communication channels

Systems and methods provide techniques for more effective and efficient predictive monitoring of a software application framework. In response, embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to enable effective and efficient predictive monitoring of a software application framework using incident signatures for the software application that are generated by using a natural language processing machine learning framework, a structured data processing machine learning model, and an incident severity level detection machine learning model.

Predictive monitoring of software application frameworks using machine-learning-based techniques

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Delhi, India

Aneesh Kundu