Natural language generation in the medical domain is heavily influenced by domain knowledge and genre-specific text characteristics. We present SemScribe, an implemented natural language generation system that produces doctor's letters, in particular descriptions of cardiological findings. Texts in this domain are characterized by a high density of information and a relatively telegraphic style. Domain knowledge is encoded in a medical ontology of about 80,000 concepts. The ontology is used in particular for concept generalizations during referring expression generation. Architecturally, the system is a generation pipeline that uses a corpus-informed syntactic frame approach for realizing sentences appropriate to the domain. The system reads XML documents conforming to the HL7 Clinical Document Architecture (CDA) Standard and enhances them with generated text and references to the used data elements. We conducted a first clinical trial evaluation with medical staff and report on the findings.

Terms, term relevances, and sentence relevances are concepts that figure in many NLP applications, such as Text Summarization. These concepts are implemented in various ways, though. In this paper, we want to shed light on the impact that different implementations can have on the overall performance of the systems. In particular, we examine the interplay between term definitions and sentence-scoring functions. For this, we define a gold standard that ranks sentences according to their significance and evaluate a range of relevant parameters with respect to the gold standard.