[Submitted on 7 Oct 2026]
Title:Clarify, Then Focus: Statement Normalization for Conversation Analytics at Scale
View a PDF of the paper titled Clarify, Then Focus: Statement Normalization for Conversation Analytics at Scale, by Mikhail L. Arbuzov (Independent researcher) and 7 other authors
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Abstract:Enterprise conversation analytics asks many questions of millions of interactions. Each question can require reconstructing what people mean and identifying which information matters, repeating costly interpretive work across the same transcripts. We propose a simple principle: clarify the text, then focus the reader. Statement normalization transforms dialogue into short, speaker-attributed statements with source references and semantic tags. The statements make meaning more explicit; the tags support selecting evidence for a particular question. Downstream models can use the full representation or a relevant subset, depending on what helps them make the decision. In an offer-suppression task on customer-service calls, normalization improves a supervised classifier without selection, while weaker prompted readers benefit from both normalization and selection. A small model can learn the normalization contract, while lightweight encoders handle tagging and downstream decisions. Sharing this preparation across questions supports an inference pipeline built entirely from small models, making analytics over millions of conversations substantially less expensive.
Comments: 20 pages, 1 figure
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
ACM classes: I.2.7
Cite as: arXiv:2610.10758 [cs.CL]
(or arXiv:2610.10758v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.10758
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mikhail Arbuzov [view email] [v1] Wed, 7 Oct 2026 18:22:55 UTC (161 KB)
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