GPT-Calls: Enhancing Call Segmentation and Tagging by Generating Synthetic Conversations via Large Language Models Article Swipe
Itzik Malkiel
,
Uri Alon
,
Yakir Yehuda
,
Shahar Keren
,
Oren Barkan
,
Royi Ronen
,
Noam Koenigstein
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2306.07941
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2306.07941
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.
Related Topics
Concepts
Computer science
Phone
Conversation
Segmentation
Artificial intelligence
Online and offline
Natural language processing
Parsing
Similarity (geometry)
Market segmentation
Process (computing)
Lexical analysis
Speech recognition
Linguistics
Philosophy
Operating system
Business
Marketing
Image (mathematics)
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2306.07941
- https://arxiv.org/pdf/2306.07941
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4380715566
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4380715566Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2306.07941Digital Object Identifier
- Title
-
GPT-Calls: Enhancing Call Segmentation and Tagging by Generating Synthetic Conversations via Large Language ModelsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-09Full publication date if available
- Authors
-
Itzik Malkiel, Uri Alon, Yakir Yehuda, Shahar Keren, Oren Barkan, Royi Ronen, Noam KoenigsteinList of authors in order
- Landing page
-
https://arxiv.org/abs/2306.07941Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2306.07941Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2306.07941Direct OA link when available
- Concepts
-
Computer science, Phone, Conversation, Segmentation, Artificial intelligence, Online and offline, Natural language processing, Parsing, Similarity (geometry), Market segmentation, Process (computing), Lexical analysis, Speech recognition, Linguistics, Philosophy, Operating system, Business, Marketing, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2023: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Calls | 51 |
| abstract_inverted_index.Sales | 189, 206 |
| abstract_inverted_index.Then, | 130 |
| abstract_inverted_index.based | 196 |
| abstract_inverted_index.calls | 3 |
| abstract_inverted_index.data, | 170 |
| abstract_inverted_index.every | 110 |
| abstract_inverted_index.found | 125 |
| abstract_inverted_index.given | 81 |
| abstract_inverted_index.group | 141 |
| abstract_inverted_index.model | 99 |
| abstract_inverted_index.novel | 48 |
| abstract_inverted_index.phase | 75, 106 |
| abstract_inverted_index.phone | 2 |
| abstract_inverted_index.sales | 199 |
| abstract_inverted_index.these | 24 |
| abstract_inverted_index.topic | 63, 95, 123, 163 |
| abstract_inverted_index.under | 186 |
| abstract_inverted_index.using | 96 |
| abstract_inverted_index.value | 7 |
| abstract_inverted_index.work, | 44 |
| abstract_inverted_index.across | 8 |
| abstract_inverted_index.anchor | 102 |
| abstract_inverted_index.domain | 132 |
| abstract_inverted_index.making | 172 |
| abstract_inverted_index.method | 158 |
| abstract_inverted_index.online | 71, 105 |
| abstract_inverted_index.phase. | 129 |
| abstract_inverted_index.sales, | 13 |
| abstract_inverted_index.scores | 114, 139 |
| abstract_inverted_index.topics | 84 |
| abstract_inverted_index.Tagging | 54 |
| abstract_inverted_index.anchors | 124 |
| abstract_inverted_index.applied | 77, 108, 135 |
| abstract_inverted_index.arduous | 30 |
| abstract_inverted_index.between | 117 |
| abstract_inverted_index.dealing | 36 |
| abstract_inverted_index.diverse | 9 |
| abstract_inverted_index.fields, | 10 |
| abstract_inverted_index.labeled | 169 |
| abstract_inverted_index.method, | 49 |
| abstract_inverted_index.offline | 69, 74, 128 |
| abstract_inverted_index.phases. | 72 |
| abstract_inverted_index.propose | 46 |
| abstract_inverted_index.require | 168 |
| abstract_inverted_index.topics. | 149 |
| abstract_inverted_index.various | 179, 203 |
| abstract_inverted_index.Dynamics | 187, 204 |
| abstract_inverted_index.accurate | 59, 155 |
| abstract_inverted_index.analysis | 22, 133 |
| abstract_inverted_index.approach | 176 |
| abstract_inverted_index.composed | 67 |
| abstract_inverted_index.customer | 14 |
| abstract_inverted_index.domains. | 180 |
| abstract_inverted_index.extended | 38 |
| abstract_inverted_index.gathered | 201 |
| abstract_inverted_index.involves | 86 |
| abstract_inverted_index.operates | 183 |
| abstract_inverted_index.paradigm | 152 |
| abstract_inverted_index.process, | 33 |
| abstract_inverted_index.proposed | 151 |
| abstract_inverted_index.provides | 153 |
| abstract_inverted_index.recorded | 25 |
| abstract_inverted_index.research | 194 |
| abstract_inverted_index.segments | 144 |
| abstract_inverted_index.service, | 15 |
| abstract_inverted_index.tenants. | 207 |
| abstract_inverted_index.vectors. | 103 |
| abstract_inverted_index.GPT-Calls | 65 |
| abstract_inverted_index.algorithm | 182 |
| abstract_inverted_index.efficient | 57, 157 |
| abstract_inverted_index.sentences | 92 |
| abstract_inverted_index.synthetic | 91 |
| abstract_inverted_index.versatile | 175 |
| abstract_inverted_index.applicable | 177 |
| abstract_inverted_index.dialogues. | 41 |
| abstract_inverted_index.especially | 34 |
| abstract_inverted_index.extracting | 101 |
| abstract_inverted_index.extraction | 164 |
| abstract_inverted_index.generating | 87 |
| abstract_inverted_index.production | 185 |
| abstract_inverted_index.separately | 112 |
| abstract_inverted_index.similarity | 116, 138 |
| abstract_inverted_index.utterances | 142 |
| abstract_inverted_index.extraction. | 64 |
| abstract_inverted_index.healthcare, | 16 |
| abstract_inverted_index.significant | 6 |
| abstract_inverted_index.(GPT-Calls), | 55 |
| abstract_inverted_index.Conversation | 190 |
| abstract_inverted_index.Segmentation | 52 |
| abstract_inverted_index.conversation | 120 |
| abstract_inverted_index.distribution | 89 |
| abstract_inverted_index.enforcement. | 19 |
| abstract_inverted_index.multifaceted | 40 |
| abstract_inverted_index.segmentation | 61, 161 |
| abstract_inverted_index.transcripted | 119 |
| abstract_inverted_index.GPT-distilled | 50 |
| abstract_inverted_index.Intelligence, | 191 |
| abstract_inverted_index.Nevertheless, | 20 |
| abstract_inverted_index.conversations | 26, 200 |
| abstract_inverted_index.Transcriptions | 0 |
| abstract_inverted_index.time-intensive | 32 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.7400000095367432 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |