TY - GEN
T1 - Harnessing GPT for Topic-Based Call Segmentation in Microsoft Dynamics 365 Sales
AU - Malkiel, Itzik
AU - Keren, Shahar
AU - Alon, Uri
AU - Barkan, Oren
AU - Koenigstein, Noam
AU - Yehuda, Yakir
AU - Ronen, Royi
N1 - Publisher Copyright: © 2023 Copyright held by the owner/author(s).
PY - 2023/10/21
Y1 - 2023/10/21
N2 - Transcriptions of phone calls hold significant value in sales, customer service, healthcare, law enforcement, and more. However, analyzing recorded conversations can be a time-consuming process, especially for complex dialogues. In Microsoft Dynamics 365 Sales, a novel system, named GPT-Calls, is applied for efficient and accurate topic-based call segmentation. GPT-Calls comprises offline and online phases. In the offline phase, the system leverages a GPT model to generate synthetic sentences and extract anchor vectors for predefined topics. This phase, performed once on a given topic list, significantly reduces the computational burden. The online phase scores the similarity between the transcribed conversation and the topic anchors from the offline phase, followed by time domain analysis to group utterances into segments and tag them with topics. The GPT-Calls scheme offers an accurate and efficient approach to call segmentation and topic extraction, eliminating the need for labeled data. It is a versatile solution applicable to various industry domains. GPT-Calls operates in production under Dynamics 365 Sales Conversation Intelligence, applied to real sales conversations from diverse Dynamics 365 Sales tenants, streamlining call analysis, and saving time and resources while ensuring accuracy and effectiveness.
AB - Transcriptions of phone calls hold significant value in sales, customer service, healthcare, law enforcement, and more. However, analyzing recorded conversations can be a time-consuming process, especially for complex dialogues. In Microsoft Dynamics 365 Sales, a novel system, named GPT-Calls, is applied for efficient and accurate topic-based call segmentation. GPT-Calls comprises offline and online phases. In the offline phase, the system leverages a GPT model to generate synthetic sentences and extract anchor vectors for predefined topics. This phase, performed once on a given topic list, significantly reduces the computational burden. The online phase scores the similarity between the transcribed conversation and the topic anchors from the offline phase, followed by time domain analysis to group utterances into segments and tag them with topics. The GPT-Calls scheme offers an accurate and efficient approach to call segmentation and topic extraction, eliminating the need for labeled data. It is a versatile solution applicable to various industry domains. GPT-Calls operates in production under Dynamics 365 Sales Conversation Intelligence, applied to real sales conversations from diverse Dynamics 365 Sales tenants, streamlining call analysis, and saving time and resources while ensuring accuracy and effectiveness.
UR - http://www.scopus.com/inward/record.url?scp=85178156301&partnerID=8YFLogxK
U2 - 10.1145/3583780.3615508
DO - 10.1145/3583780.3615508
M3 - منشور من مؤتمر
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 5246
EP - 5247
BT - CIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023
Y2 - 21 October 2023 through 25 October 2023
ER -