Daniel Lee


Answering Chinese Elementary School Social Studies Multiple Choice Questions
Chao-Chun Liang | Daniel Lee | Meng-Tse Wu | Hsin-Min Wang | Keh-Yih Su
International Journal of Computational Linguistics & Chinese Language Processing, Volume 26, Number 2, December 2021


Reference and Document Aware Semantic Evaluation Methods for Korean Language Summarization
Dongyub Lee | Myeong Cheol Shin | Taesun Whang | Seungwoo Cho | Byeongil Ko | Daniel Lee | EungGyun Kim | Jaechoon Jo
Proceedings of the 28th International Conference on Computational Linguistics

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.


University of Houston at CL-SciSumm 2016: SVMs with tree kernels and Sentence Similarity
Luis Moraes | Shahryar Baki | Rakesh Verma | Daniel Lee
Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL)