Summarizing Medical Conversations via Identifying Important Utterances

Proceedings of the 28th International Conference on Computational Linguistics, pages 717–729 Barcelona, Spain (Online), December 8-13, 2020 717 Summarizing Medical Conversations via Identifying Important Utterances Yan Song♠♥∗, Yuanhe Tian♥∗, Nan Wang♣, Fei Xia♥ ♠The Chinese University of Hong Kong (Shenzhen) ♥Shenzhen Research Institute of Big Data ♥University of Washington ♣Hunan University ♠songyan@cuhk.edu.cn ♥{yhtian, fxia}@uw.edu ♣ncwang@hnu.edu.cn Abstract Summarization is an important natural language processing (NLP) task in identifying key infor- mation from text. For conversations, the summarization systems need to extract salient contents from spontaneous utterances by multiple speakers. In a special task-oriented scenario, namely medical conversations between patients and doctors, the symptoms, diagnoses, and treatments could be highly important because the nature of such conversation is to find a medical solution to the problem proposed by the patients. Especially consider that current online medical platforms provide millions of public available conversations between real patients and doctors, where the patients propose their medical problems and the registered doctors offer diagnosis and treatment, a conversation in most cases could be too long and the key information is hard to be located. There- fore, summarizations to the patients’ problems and the doctors’ treatments in the conversations can be highly useful, in terms of helping other patients with similar problems have a precise reference for potential medical solutions. In this paper, we focus on medical conversation summarization, using a dataset of medical conversations and corresponding summaries which were crawled from a well-known online healthcare service provider in China. We propose a hierarchical encoder-tagger model (HET) to generate summaries by identifying important utterances (with respect to problem proposing and solving) in the conversations. For the particular dataset used in this study, we show that high-quality summaries can be generated by extracting two types of utterances, namely, problem statements and treatment recommendations. Experimental results demonstrate that HET outperforms strong baselines and models from previous studies, and adding conversation-related features can further improve system performance.1 1 Introduction Applying natural language processing (NLP) techniques to the medical field is a prevailing trend nowadays and has great potential in many applications, such as key information extraction in medical literature (Kim et al., 2011; Dernoncourt et al., 2017; ˇSeva et al., 2018), risk factor identification in electronic health records (Chang et al., 2015; Cormack et al., 2015; Cheng et al., 2016), and medical question answering (Pampari et al., 2018; Tian et al., 2019). As the demand for healthcare services increases greatly in the past decades,2 it is urgent to improve the quality and efficiency of healthcare, reduce workload and mental stress of health providers and increase patient satisfaction. Recently, Internet-based healthcare platforms such as online doctor systems and doctor-patient cyber communities have been increasingly used by patients and health professionals with the hope that they would alleviate the ever-increasing demands for healthcare services and reduce the inaccessibility of services caused by geographical and socio-economic barriers. In such platforms, a patient can start a conversation to a registered doctor by typing their medical problems and then the doctor may ask the …

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