HealthAlignSumm : Utilizing Alignment for Multimodal Summarization of Code-Mixed Healthcare Dialogues

Abstract

As generative AI progresses, collaboration be-tween doctors and AI scientists is leading to thedevelopment of personalized models to stream-line healthcare tasks and improve productivity.Summarizing doctor-patient dialogues has be-come important, helping doctors understandconversations faster and improving patient care.While previous research has mostly focused ontext data, incorporating visual cues from pa-tient interactions allows doctors to gain deeperinsights into medical conditions. Most of thisresearch has centered on English datasets, butreal-world conversations often mix languagesfor better communication. To address the lackof resources for multimodal summarization ofcode-mixed dialogues in healthcare, we devel-oped the MCDH dataset. Additionally, we cre-ated HealthAlignSumm, a new model that in-tegrates visual components with the BART ar-chitecture. This represents a key advancementin multimodal fusion, applied within both theencoder and decoder of the BART model. Ourwork is the first to use alignment techniques,including state-of-the-art algorithms like DirectPreference Optimization, on encoder-decodermodels with synthetic datasets for multimodalsummarization. Through extensive experi-ments, we demonstrated the superior perfor-mance of HealthAlignSumm across severalmetrics validated by both automated assess-ments and human evaluations. The datasetMCDH and our proposed model HealthAlign-Summ will be available in this GitHub accounthttps://github.com/AkashGhosh/HealthAlignSumm-Utilizing-Alignment-for-Multimodal-Summarization-of-Code-Mixed-Healthcare-DialoguesDisclaimer: This work involves medical im-agery based on the subject matter of the topic.