Reference and Document Aware Semantic Evaluation Methods for Korean Language Summarization
Table 2: Performance of the summarization model on the DAUM/NEWS dataset.
['Model', 'Proposed Evaluation Metrics  [ITALIC] s( [ITALIC] p, [ITALIC] r)', 'Proposed Evaluation Metrics  [ITALIC] s( [ITALIC] p, [ITALIC] d)', 'Proposed Evaluation Metrics  [ITALIC] RDASS', 'ROUGE 1', 'ROUGE 2', 'ROUGE L']
The abstractive summarization model is based on the neural architecture of [liu2019text]. We trained the summarization model on the Daum/News dataset. To evaluate the summarization model, we used ROUGE and the proposed evaluation metrics. The fine-tuned FWA-SBERT was then used to evaluate the proposed semantic scores (s(p,r), s(p,d), and RDASS).