Joint Dialogue Topic Segmentation and Categorization: A Case Study on Clinical Spoken Conversations (2023)

Abstract: Utilizing natural language processing techniques in clinical conversations is effective to improve the efficiency of health management workflows for medical staff and patients. Dialogue segmentation and topic categorization are two fundamental steps for processing verbose spoken conversations and highlighting informative spans for downstream tasks. However, in practical use cases, due to the variety of segmentation granularity and topic definition, and the lack of diverse annotated corpora, no generic models are readily applicable for domain-specific applications. In this work, we introduce and adopt a joint model for dialogue segmentation and topic categorization, and conduct a case study on healthcare follow-up calls for diabetes management; we provide insights from both data and model perspectives toward performance and robustness.

## introduction the massive records of clinical communication, especially the longitudinal follow-up calls, can be used to scrutinize novel insights into medical history, treatment plans, and customized education @xcite ; but it is time-consuming and requires domain knowledge for manual operation. therefore, there has been growing interest in utilizing speech and natural language techniques to analyze and distill information from clinical conversations @xcite @xcite . while spoken conversations are often loosely structured, in task-oriented scenarios, interlocutors calibrate the dialogue flow to cover targeted topics and agendas @xcite . moreover, when large language models @xcite are applied, processing the verbose conversations will substantially increase the computational complexity and cost. on the other hand, dialogue segmentation and topic categorization @xcite are useful to handle lengthy inputs, reduce data noise by excluding the task-irrelevant segments, and improve the efficiency of downstream tasks @xcite . more specifically, dialogue segmentation is to extract the structural information by splitting the whole session into topicallycoherent segments @xcite , and topic categorization labels each segment with a particular type, providing features for fine-grained semantic understanding @xcite . different from documents, human conversations include ubiquitous verbal and vernacular expressions, along with disfluencies, thinking aloud, and repetition. this leads to lower information density @xcite and more topic drifting. the coherence-based methods typically applied to passages cannot perform well on spoken dialogues. moreover, since there are few corpora constructed with the dedicated annotation, most existing generic (both supervised @xcite and unsupervised (xing and carenini, 2021)) models cannot meet the requirements of real-world applications and provide reliable system outputs. this is because (1) there is no unified segmentation granularity across different data resources, and (2) the variety of topic definitions increases the difficulty of domain adaptation, especially where language resources are limited. in this work, we conduct a case study on a clinical conversation scenario. because of the chronic nature of diabetes and its associated complications, diabetes requires constant attention and regular follow-up actions @xcite . nurses schedule calls with patients to track their compliance status and health condition, and provide customized coaching and advice @xcite . to facilitate the communication process, dialogues are organized according to a checklist or medical protocol @xcite . however, due to the characteristics of spoken dialogues such as topic drifting and verbosity, the important information is scattered across the whole conversation, which renders it a representative use case for dialogue segmentation and topic categorization (as the example shown in figure 1 ). since no existing generic models meet the requirements of our domain-specific application, we investigate a data-driven approach for the clinical conversation processing task, and our contributions of this work are as follows: ## experimental results & analysis we conduct extensive experiments to assess the model on our domain-specific application. ## related work topic structure analysis plays a pivotal role in dialogue understanding @xcite . dialogue segmentation is similar to monologue segmentation, and aims to split a dialogue session into topically-coherent units. various approaches originally proposed to process documents can also be applied to the dialogue domain. due to a lack of training data, there are many unsupervised models, that exploit various linguistic features such as the word co-occurrence statistics @xcite , topical distribution @xcite to measure the sentence similarity between utterances, so that topical or semantic changes can be detected. more recently, with the availability of large-scale corpora sampled from wikipedia, by taking the section mark as the ground-truth segment boundary @xcite , there has been a rapid growth in supervised approaches for monologue topic segmentation, especially neural-based approaches @xcite . in practical use cases, supervised solutions are favored, as they present robust performance and higher learning efficiency. language understanding of clinical conversation has attracted a plethora of research work on indepth analysis regarding clinician-patient communications @xcite @xcite . more recent work has included the utterances classification according to soap sections @xcite , dialogue action detection @xcite , named entity recognition @xcite , information extraction @xcite , extractive @xcite and abstractive summarization @xcite . though the downstream language understanding tasks are not explored in this work, dialogue segmentation and topic categorization are beneficial for those tasks by reducing the computational complexity and filtering redundant utterances. ## conclusion the variety of segmentation granularity and topic definition poses challenges to domain-specific dialogue modeling and low-resource training. in this work, we investigated a joint model for dialogue segmentation and topic categorization. from our real-world case study on health management calls, we found that the nurse-to-patient conversations are shown to be topically organized, and modeling conversational features is beneficial for improving performance in practical clinical scenarios.