Reference and Document Aware Semantic Evaluation Methods for Korean Language Summarization

Autor:	Lee, Dongyub, Shin, Myeongcheol, Whang, Taesun, Cho, Seungwoo, Ko, Byeongil, Lee, Daniel, Kim, Eunggyun, Jo, Jaechoon
Rok vydání:	2020
Předmět:	Computer Science - Computation and Language Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: COLING 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2005.03510 Zobrazit plný text záznamu View this record from Arxiv