Automatic summarization of medical conversations, a review (2019)

Abstract: ion et pour l’analyse du dialogue. Nous décrivons aussi les utilisation du Traitement Automatique des Langues dans le domaine médical. A BSTRACT Conversational analysis plays an important role in the development of simulation devices for the training of health professionals (doctors, nurses). Our goal is to develop an original automatic synthesis method for medical conversations between a patient and a healthcare professional, based on recent advances in summarization using convolutional and recurrent neural networks. The proposed method must be adapted to the specific problems related to the synthesis of dialogues. This article presents a review of the different methods for extractive and abstractive summarization, and for dialogue analysis. We also describe the use of Natural Language Processing in the medical field.

## introduction spoken medical dialogues are common and useful among doctors, nurses and patients. automatically gathering the information of these dialogues is relevant for the medical practitioners and patients. for instance, the doctor can generate a medical history without omitting information, and a patient can review the diagnostic and the treatment later in time. however, none of them want a full record or transcript all dialogues. besides, doctors and nurses have many interactions with different patients all day long, so it wouldn't be productive that they now have to deal with all these records. accordingly, a challenge for natural language processing (nlp) arises : obtaining automatic summaries from medical conversations. according to the above, our final goal is to develop an automatic summarization method for medical conversations between patients and doctors or nurses. however, our work is related to a project in our laboratory implementing a system for conversations between a virtual patient (chatbot) and medicine students @xcite . in this serious game, the student establishes a conversation with the chatbot to establish its diagnostic about the medical condition of the patient. the proposed method should be adapted to the specific problems of summarizing dialogues based on recent methods used in automatic summarization, such as recurrent neural networks. the hypothesis is that pertinent segments of dialogue might be detected and abstracted for their inclusion in a summary through deep learning. we suppose that, by identifying pertinent blocks of dialogues, it would be easier to detect important clinical issues and discard issues fragments like greetings, acknowledgments, social concerns, etc. in this paper we review the work related to our goal of summarizing medical conversations. we first describe the two main types of summaries : extractive and abstractive. extractive methods consist of copying chunks, usually sentences, from the original text. abstractive methods are able to generate new words and sentences. in the literature there are more extractive approaches since they were easier than abstractive methods, even if the latter are becoming conceivable @xcite . this paper describes different techniques for each one. besides, it also reviews the specific mechanisms used in research on dialogue summarization. the last section is crucial because it describes different approaches and works in nlp in the medical domain. these approaches include medical conversation systems and summarization in the medical domain. we discuss the challenges they represent. ## methods for summarization in this section we will mention different types of approaches that researchers have been working along the time to get automatic summaries. ## nlp in medical domain in medical domain, doctors and nurses have a lot of information about patients : medical records, appointments, schedule of activities, etc. all information must be organized, and manually involves a lot of time and human effort. besides, the amount of information increases each second and there is sometimes not enough people. currently, there are algorithms capable of helping nurses and doctors to summarize and organize information. however the medical domain is huge. some works are focused on medical diagnostic tools, such as @xcite other researches have developed intelligent agents, such as @xcite to help people at home. people can talk to the systems using natural language. the following subsections concentrate on conversational and summarization systems in the medical domain. ## discussion we have presented works about medical summarization divided in three sections : methods for summarization, nlp for dialogue analysis and medical conversation systems. in the first section, the most used methods on extractive and abstractive summaries have been reviewed. the second section presented works focused on dialogue summaries, and the challenge that they present. in the last section we cited works to help people at home, and software to detect depression, suicide and mental diseases. after this review of the bibliography, we realized that there are a lot of articles about automatic summarization, but the majority are focused on scientific articles, news, etc. and there are few works on conversations. furthermore, there is even less works on medical dialogue. one of the most important reason is that it is not easy to get datasets. similarly, we find more articles on extractive summaries than abstractive summaries, even if the current tendency is clearly to switch to abstractive using neural networks. our future work is based on the model by @xcite . we will apply this technique on the pubmed dataset (medical articles). we decided to work with this algorithm because they used a hybrid pointer-generator network to decide if a specific word is copied from the source text or if a new word from the vocabulary will be generated. our hypothesis is that pointer-generator networks can be useful on the medical domain because we can generate new words from its specialized vocabulary. this will make easier to handle medical terms. besides, they proposed a novel variant of coverage based on @xcite to avoid repetitions in summaries. as noted above, it will be very important to insure that the generated text doesn't modify the sense of the original dialogue. we will also make the algorithm able to handle long texts while it was designed only for the first words of a news article. our second approach is to work on the ami corpus developed by @xcite , a dataset of 100 hours of multi-modal meetings recordings. we will develop a mechanism to generate abstractive summaries from dialogues. we decided to work on ami dataset to get automatic summaries from real conversations and consider aspects that we can find only in dialogues such as greetings, speakers' turns, etc. however, our main idea is to develop a mechanism to be able to get abstractive summaries from medical dialogues. to achieve this task, it's necessary to work with both approaches : summarization of dialogues and summarization in the medical domain.