  Text summarization refers to the process that generates a shorter form of
text from the source document preserving salient information. Many existing
works for text summarization are generally evaluated by using recall-oriented
understudy for gisting evaluation (ROUGE) scores. However, as ROUGE scores are
computed based on n-gram overlap, they do not reflect semantic meaning
correspondences between generated and reference summaries. Because Korean is an
agglutinative language that combines various morphemes into a word that express
several meanings, ROUGE is not suitable for Korean summarization. In this
paper, we propose evaluation metrics that reflect semantic meanings of a
reference summary and the original document, Reference and Document Aware
Semantic Score (RDASS). We then propose a method for improving the correlation
of the metrics with human judgment. Evaluation results show that the
correlation with human judgment is significantly higher for our evaluation
metrics than for ROUGE scores.
