GPT-Calls: Enhancing Call Segmentation and Tagging by Generating
  Synthetic Conversations via Large Language Models
Transcriptions of phone calls are of significant value across diverse fields,
such as sales, customer service, healthcare, and law enforcement. Nevertheless,
the analysis of these recorded conversations can be an arduous and
time-intensive process, especially when dealing with extended or multifaceted
dialogues. In this work, we propose a novel method, GPT-distilled Calls
Segmentation and Tagging (GPT-Calls), for efficient and accurate call
segmentation and topic extraction. GPT-Calls is composed of offline and online
phases. The offline phase is applied once to a given list of topics and
involves generating a distribution of synthetic sentences for each topic using
a GPT model and extracting anchor vectors. The online phase is applied to every
call separately and scores the similarity between the transcripted conversation
and the topic anchors found in the offline phase. Then, time domain analysis is
applied to the similarity scores to group utterances into segments and tag them
with topics. The proposed paradigm provides an accurate and efficient method
for call segmentation and topic extraction that does not require labeled data,
thus making it a versatile approach applicable to various domains. Our
algorithm operates in production under Dynamics 365 Sales Conversation
Intelligence, and our research is based on real sales conversations gathered
from various Dynamics 365 Sales tenants.
648920dd9ab28735f19aafae
Itzik Malkiel
648920dd9ab28735f19aafaf
Uri Alon
admin_assigned
2023-06-14T11:55:32.702Z
62cd8153248f9e6bc20ab250
https://cdn-avatars.huggingface.co/v1/production/uploads/1657635637504-62cd8153248f9e6bc20ab250.jpeg
Uri Alon
user
urialon
648920dd9ab28735f19aafb0
Yakir Yehuda
admin_assigned
2023-06-14T11:55:59.273Z
63904cd500fb8ec4a424634f
/avatars/232f51fe49c7d8d491655ad2979009b9.svg
yakir yehuda
user
yakiryehuda
648920dd9ab28735f19aafb1
Shahar Keren
648920dd9ab28735f19aafb2
Oren Barkan
648920dd9ab28735f19aafb3
Royi Ronen
648920dd9ab28735f19aafb4
Noam Koenigstein
GPT-Calls: Enhancing Call Segmentation and Tagging by Generating
  Synthetic Conversations via Large Language Models
Transcriptions of phone calls are of significant value across diverse fields,
such as sales, customer service, healthcare, and law enforcement. Nevertheless,
the analysis of these recorded conversations can be an arduous and
time-intensive process, especially when dealing with extended or multifaceted
dialogues. In this work, we propose a novel method, GPT-distilled Calls
Segmentation and Tagging (GPT-Calls), for efficient and accurate call
segmentation and topic extraction. GPT-Calls is composed of offline and online
phases. The offline phase is applied once to a given list of topics and
involves generating a distribution of synthetic sentences for each topic using
a GPT model and extracting anchor vectors. The online phase is applied to every
call separately and scores the similarity between the transcripted conversation
and the topic anchors found in the offline phase. Then, time domain analysis is
applied to the similarity scores to group utterances into segments and tag them
with topics. The proposed paradigm provides an accurate and efficient method
for call segmentation and topic extraction that does not require labeled data,
thus making it a versatile approach applicable to various domains. Our
algorithm operates in production under Dynamics 365 Sales Conversation
Intelligence, and our research is based on real sales conversations gathered
from various Dynamics 365 Sales tenants.
GPT-Calls, an efficient and accurate method for call segmentation and topic extraction, utilizes synthetic sentences and time domain analysis to tag conversations without labeled data.
GPT(Calls) synthetic sentences anchor vectors similarity scores time domain analysis call segmentation topic extraction