Training and using a deep learning model for transcript topic segmentation

A computerized method comprising:

• obtaining, by a processor, a communication transcript including a set of utterances, wherein the obtained communication transcript is a text data set;
• dividing, by the processor, the set of utterances into a plurality of utterance windows of a defined window size, wherein each utterance window of the plurality of utterance windows includes a different subset of utterances of the set of utterances, and wherein each utterance of the set of utterances is included in at least one utterance window of the plurality of utterance windows;
• for each utterance window of the plurality of utterance windows, classifying, by the processor, each utterance in the utterance window as a topic boundary or a non-boundary using a deep learning model applied to the utterance window;
• identifying, by the processor, topic segments of the communication transcript based on utterances of the set of utterances that are classified as topic boundaries;
• and
• generating, by the processor, a communication transcript summary using the communication transcript and the identified topic segments.

One or more computer storage media having computer-executable instructions that, upon execution by a processor, cause the processor to at least:

• obtain a communication transcript including a set of utterances, wherein the obtained communication transcript is a text data set;
• divide the set of utterances into a plurality of utterance windows of a defined window size, wherein each utterance window of the plurality of utterance windows includes a different subset of utterances of the set of utterances, and wherein each utterance of the set of utterances is included in at least one utterance window of the plurality of utterance windows;
• for each utterance window of the plurality of utterance windows, classify each utterance in the utterance window as a topic boundary or a non-boundary using a deep learning model applied to the utterance window;
• identify topic segments of the communication transcript based on utterances of the set of utterances that are classified as topic boundaries;
• and
• generate a communication transcript summary using the communication transcript and the identified topic segments.