SEGLLM: Topic-Oriented Call Segmentation Via LLM-Based Conversation Synthesis

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 long and multifaceted dialogues. In this work, we propose a novel method, which we name SegLLM, for efficient and accurate call segmentation and topic extraction. SegLLM 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 large language model (LLM). 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. 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.