Abstract: A method for summarizing dialogs may include obtaining an agent text stream and a customer text stream, segmenting the agent text stream and customer text stream into sentences, and labeling sentences associated with the segmented agent text stream and the segmented customer text stream. The method may further include extracting sentences from the agent text stream and the customer text stream based upon frequencies of appearance of words and terms of interest; generating an agent summary paragraph based on the extracted sentences from the agent text stream, and generating a customer summary paragraph based on the extracted sentences from the customer text stream. The method may identify keywords associated with each of the agent summary paragraph and the customer summary paragraph.

Abstract: A method for summarizing dialogs may include obtaining an agent text stream and a customer text stream, segmenting the agent text stream and customer text stream into sentences, and labeling sentences associated with the segmented agent text stream and the segmented customer text stream. The method may further include extracting sentences from the agent text stream and the customer text stream based upon frequencies of appearance of words and terms of interest; generating an agent summary paragraph based on the extracted sentences from the agent text stream, and generating a customer summary paragraph based on the extracted sentences from the customer text stream. The method may identify keywords associated with each of the agent summary paragraph and the customer summary paragraph.

Abstract: A method for summarizing dialogs may include obtaining an agent text stream and a customer text stream, segmenting the agent text stream and customer text stream into sentences, and labeling sentences associated with the segmented agent text stream and the segmented customer text stream. The method may further include extracting sentences from the agent text stream and the customer text stream based upon frequencies of appearance of words and terms of interest; generating an agent summary paragraph based on the extracted sentences from the agent text stream, and generating a customer summary paragraph based on the extracted sentences from the customer text stream. The method may identify keywords associated with each of the agent summary paragraph and the customer summary paragraph.

Abstract: A method for summarizing dialogs may include obtaining an agent text stream and a customer text stream, segmenting the agent text stream and customer text stream into sentences, and labeling sentences associated with the segmented agent text stream and the segmented customer text stream. The method may further include extracting sentences from the agent text stream and the customer text stream based upon frequencies of appearance of words and terms of interest; generating an agent summary paragraph based on the extracted sentences from the agent text stream, and generating a customer summary paragraph based on the extracted sentences from the customer text stream. The method may identify keywords associated with each of the agent summary paragraph and the customer summary paragraph.

Abstract: A computer device may include a memory storing instructions and processor configured to execute the instructions to obtain a text file generated based on a communication session; select topics for classifying communication sessions; and apply one or more trained machine learning models to the obtained text file to determine one or more most likely topics for the communication session. The processor may be further configured to apply regular expression binary classifiers, each associated with a different topic, to the obtained text file to determine a likelihood the communication session is associated with a particular topic; select a topic for the communication session based on the determined one or more most likely topics and the determined likelihood the communication session is associated with the particular topic; and classify the communication session based on the selected topic.

Abstract: A computer device may include a memory storing instructions and processor configured to execute the instructions to obtain a text file generated based on a communication session; select topics for classifying communication sessions; and apply one or more trained machine learning models to the obtained text file to determine one or more most likely topics for the communication session. The processor may be further configured to apply regular expression binary classifiers, each associated with a different topic, to the obtained text file to determine a likelihood the communication session is associated with a particular topic; select a topic for the communication session based on the determined one or more most likely topics and the determined likelihood the communication session is associated with the particular topic; and classify the communication session based on the selected topic.