Summary2vec: learning semantic representation of summaries for healthcare analytics

The ever-increasing amount of text data makes it challenging for humans to extract the required information. This problem is even more vital for the medical domain, where accessing up-to-date information is essential. Physicians and researchers face diverse and extensive medical sources such as medical journal articles, websites, or patient records. Therefore, they require to analyze them based on their interests and needs quickly. Intelligent content summarization approaches are assisting tools in such situations to provides an overview of a set of documents. However, the summary is required to be tailored to two different users type preferences: the physician and the patient. This paper proposes a novel embedding method, called Summary2vec, where each summary is presented by a fixed -length vector covering various aspects of information space. Summary2vec is remedial to design automatic services for various analytic purposes that require information-seeking activity. We leverage Summary2vec to produce a hierarchical summarization structure to enable users navigating through the hierarchy to gain more elaborated information upon request by engaging them.