Summarizing Medical Conversations via Identifying Important Utterances (2020)

Abstract: Summarization is an important natural language processing (NLP) task in identifying key information 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. Therefore, 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.

## 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 @xcite @xcite , risk factor identification in electronic health records @xcite @xcite , and medical question answering @xcite . 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 patients to specify his/her problem (e.g., symptoms, treatment has been taken, etc.). since the conversation is asynchronous, it is possible that one speaker (either the d 内服药物之外还可以中药外洗.这个方法 也有一定的效果。蚕砂、苦参、芒硝、白 矾、荆芥准备二十克，把这些药一起煎了 进行外洗，一天二次。 in addition to taking medicines, you can also wash the skin with chinese medicine, which has some effect. use the decoction of silkworm litter, sophora flavescens, glauber's salt, alum, and nepeta, 20 grams for each, to wash your skin twice a day. dt sum1 胆碱能性荨麻疹怎么治疗 how to treat cholinergic urticaria and measles you need to avoid triggers of cholinergic urticaria. keep your body cool and avoid sweating, mental stress, hot drink, alcoholic beverages, etc. in addition to taking medicines, you can also wash the skin with chinese medicine, which has some effect. use the decoction of silkworm litter, sophora flavescens, glauber's salt, alum, and nepeta, 20 grams for each, to wash your skin twice a day. ## a corpus of medical conversations medical conversation is a type of task-oriented conversation. different from ordinary conversations in which topics are often fluid, in task-specific conversations, participants interact to accomplish a projected set of goals and sub-goals @xcite . specifically, for conversations in the medical domain from online medical platforms, the projected goal is for the doctor to diagnose and offer treatment recommendation for the patient's problem @xcite @xcite . particularly in china, many platforms make such medical conversations publicly available so that new patients with similar problem can search relevant conversations and find helpful information from them. therefore, summarization of the patient's problem and doctor's recommendations in a conversation could be highly important because such summaries can help the new patients locate the key information, especially when a conversation is too long. to conduct such summarization, a straightforward solution is to identity the important utterances that contain key information for problem statements or treatment recommendations. however, limited corpus can be found to train such summarization model, especially for chinese. therefore, we develop a corpus in chinese for medical conversation summarization and illustrate the details in the following text. the raw data the original data are crawled from one of the most well-known online health provider platforms foot_0 in china, under a section called "frequently inquired health problems." foot_1 in these conversations, patients consult registered doctors foot_2 about some health problems; doctors help them to determine the nature of the problems, provide treatment recommendations, and/or advise them to seek further medical attention from other health facilities. instead of isolated question-answer segments or part of the conversations, this data contain full conversations between patients and doctors, covering the entire interaction process. in addition to dialogues, each conversation contains meta information such as the type of disease and the corresponding hospital department, as well as the speakership of the utterances in conversation. 6 many (but not all) conversations include a summary added by doctors after the conversations are conducted. the summary has two parts: sum1 describes the medical problem that the patient has; sum2 summarizes the doctor's diagnosis or treatment recommendations. sum2 is of two types: type a (we denote it as sum2-a) is the concatenation of a few utterances in the conversation, whereas type b (we denote it as sum2-b) is a more concise summary written by the doctor and may contain text that does not appear in the conversation. in all, we crawled 109,850 conversations from 23 hospital departments or sub-divisions, and the conversations cover 1,839 disease types, which forms our raw corpus. among them, only half of them contains both sum1 and sum2. this again emphasizes the necessity of this summarization task, because if we can automatically generate the missing summaries for problem statement and treatment recommendations, new patients may have more references when they search conversations that are relevant to their problem. ## summarization via tagging to model conversation, a common approach is to use a two-level hierarchical sequential model @xcite , in which a conversation may be modeled as a sequence of utterances, and each utterance is modeled as a sequence of words or characters. using such hierarchical models, conventional studies mainly focused on conversation generation @xcite @xcite , where a decoder is employed to generate responses conditioning upon the vectors encoded from the hierarchical modeling of previous utterances. for our dataset, there is a big overlap between utterances and the summaries; for instance, as shown in to further enhance our model, we adopt memory networks @xcite to incorporate the information from relevant utterances in the conversation. therefore, our model is a hierarchical encoder-tagger (het) with the memory module applied between the token-level and utterance-level encoders, which is illustrated in figure 2 . also, it is worth noting that our method can generate the two types of summaries simultaneously, since they directly come from the predicted pd/dt/ot labels. in the following texts, we firstly introduce the memory module and then elaborate the whole hierarchical tagging process with the memories. ## conclusion and future work in this paper, we proposed a new task of medical conversation summarization, which is performed by identifying important utterances in the conversation between patients and doctors. based on the real data from a chinese online medical service provider, a hierarchical encoder-tagger model (het), which is enhanced by the memory module, was proposed to tag each utterance in a conversation with problem statement or treatment recommendation. the labeled utterances are then concatenated to form summaries. the experimental results demonstrate the validity of our approach to medical conversation summarization via identifying important utterances on the proposed dataset. for future work, we plan to perform further key information extraction on the conversation summaries from similar medical problems, so that we can obtain relevant information such as symptoms and treatment recommendations to a particular medical problem and help new patients to locate more precise references that are covered in many cases.