title: GPT-Calls: Enhancing Call Segmentation and Tagging by Generating
    Synthetic Conversations via Large Language Models
summary: 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.
paper_authors._id: 648920dd9ab28735f19aafae
paper_authors.name: Itzik Malkiel
paper_authors._id: 648920dd9ab28735f19aafaf
paper_authors.name: Uri Alon
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-14T11:55:32.702Z
paper_authors._id: 62cd8153248f9e6bc20ab250
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1657635637504-62cd8153248f9e6bc20ab250.jpeg
paper_authors.fullname: Uri Alon
paper_authors.type: user
paper_authors.user: urialon
paper_authors._id: 648920dd9ab28735f19aafb0
paper_authors.name: Yakir Yehuda
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-14T11:55:59.273Z
paper_authors._id: 63904cd500fb8ec4a424634f
paper_authors.avatarUrl: /avatars/232f51fe49c7d8d491655ad2979009b9.svg
paper_authors.fullname: yakir yehuda
paper_authors.type: user
paper_authors.user: yakiryehuda
paper_authors._id: 648920dd9ab28735f19aafb1
paper_authors.name: Shahar Keren
paper_authors._id: 648920dd9ab28735f19aafb2
paper_authors.name: Oren Barkan
paper_authors._id: 648920dd9ab28735f19aafb3
paper_authors.name: Royi Ronen
paper_authors._id: 648920dd9ab28735f19aafb4
paper_authors.name: Noam Koenigstein
paper_title: GPT-Calls: Enhancing Call Segmentation and Tagging by Generating
    Synthetic Conversations via Large Language Models
paper_summary: 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.
paper_ai_keywords: GPT-distilled Calls Segmentation and Tagging (GPT-Calls) GPT model anchor vectors similarity scores time domain analysis utterances segments topics Dynamics 365 Sales Conversation Intelligence