Reference and Document Aware Semantic Evaluation Methods for Korean Language Summarization

• DOI: 10.18653/v1/2020.coling-main.491
• Publication Date: 2021-01-08T13:58:31Z
• Authors: Dongyub Lee; Myeong Cheol Shin; Taesun Whang; Seungwoo Cho; Byeongil Ko; Daniel Lee; EungGyun Kim; Jaechoon Jo
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Science - Computation and Language; Machine Learning (stat.ML); 02 engineering and technology; 01 natural sciences; Machine Learning (cs.LG); Statistics - Machine Learning; 0202 electrical engineering, electronic engineering, information engineering; Computation and Language (cs.CL); 0105 earth and related environmental sciences

COLING 2020<br/>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.<br/>