Abstract: Embodiments are disclosed for real-time copyfitting using a shape of a content area and input text. A content area and an input text for performing copyfitting using a trained classifier is received. A number of remaining characters in the content area is computed in real-time using the input, the computing performed in response to receiving additional input text, wherein computing, in real-time, the number of remaining characters in the content area using the input text includes generating, by the trained classifier, a set of weights including a first set of one or more weights for the input text and a second set of one or more weights for the content area. The first set of one or more weights, the second set of one or more weights, the input text, and the additional input text, and a copyfitting parameter indicating a number of additional characters to be fitted into the content area are determined based on the content area.

Abstract: 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.

Abstract: Methods, apparatuses, or computer program products provide for generating context summaries of multi-party communication channels. A summary trigger request associated with a multi-party communication channel identifier may be received. A plurality of communication data objects from a selected multi-party communication channel may be extracted. 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 stored in a summary storage location. A member event indication may be received. The member event indication may comprise the multi-party channel identifier and a member profile identifier. The abstractive context summary for the selected multi-party communication channel may be retrieved from the summary storage location.