카카오(이동엽)와 카카오엔터프라이즈 AI Lab(신명철, 조승우, 고병일, 이다니엘, 김응균), 고려대학교(황태선), 한신대학교(조재춘)가 공동으로 연구한 성과를 정리한 논문 'Reference and Document Aware Semantic Evaluation Methods for Korean Language Summarization(이하 RDASS)'이 국제전산언어학술대회(이하 COLING)에 게재 승인됐습니다. COLING은 자연어처리(NLP) 및 언어학을 대표하는 국제 학회로, 올해 제출된 2,319편의 논문 중 644개의 논문이 통과됐습니다. 1
RDASS는 공동 연구팀이 텍스트 요약 모델의 성능을 측정하기 위해 고안한 새로운 평가 척도입니다. 기존의 평가 척도인 ROUGE와 비교 실험을 진행한 결과, RDASS가 실제 사람과 유사한 평가 결과를 냈습니다. 또한 공동 연구팀은 정성적 평가를 통해 RDASS가 <모델이 생성한 요약문, 실제 정답 요약문>의 의미적 유사도를 더 잘 반영할 수 있다는 점을 확인했습니다.
공동 연구팀은 영어 텍스트 요약 태스크에서의 추가 검증과 같은 RDASS의 상대적 효용성을 입증하는 실험을 진행하는 한편, 기계가 생성한 요약문의 참 또는 거짓을 판단하는 요소도 반영하는 연구를 진행할 계획입니다.
Text summarization refers to the process that generates a shorter form of text from the source document preserving salient information. Recently, many models for text summarization have been proposed. Most of those models were evaluated 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 expresses 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.
To conduct reference and document aware semantic evaluation, we first compute cosine similarity score between the generated summary with the original document s(p,d). Then we compute cosine similarity score between the generated summary with the reference summary s(p,r). Given a reference and source document, the reference-document-aware semantic score (RDASS) of the generated summary is defined by averaging s(p,r) and s(p,d). The reference-document-aware evaluation metric model can be further trained to cap-ture more contextualized information from both on reference summary and document.
Figures (1a) and (1b) show the Pearson correlation and Kendall rank, respectively, of the proposed evaluation metrics with human judgment on the 200 sampled summaries. Pearson correlation measures whether the two variables are linearly related, where 1 indicates positive linear correlation and -1 indicates negative linear correlation. And Kendall rank measures the rank correlation of the two variables, where 1 indicates two variables are similar and -1 indicates dissimilar. Both correlation measure methods are widely used in summarization tasks to analyze correlation with human judgment. In the Pearson correlation matrix, the correlation with human judgment was significantly higher for the proposed evaluation metrics than for ROUGE scores. Additionally, in the Kendall rank matrix, the proposed evaluation metrics showed highest correlation with human judgment than did the ROUGE scores. Among the proposed evaluation metrics, s(p, r) showed higher performance than s(p, d) and RDASS showed the highest correlation with human judgment. These results indicate that the proposed evaluation metrics can reflect deep semantic meaning overcoming the limitations of ROUGE which based on n-gram overlap.
- International Conference on Computational Linguistics의 약자 [본문으로]