Multi-Knowledge-Enhanced Model for Korean Abstractive Text Summarization

Text summarization plays a crucial role in processing extensive textual data, particularly in low-resource languages such as Korean. However, abstractive summarization faces persistent challenges, including semantic distortion and inconsistency. This study addresses these limitations by proposing a multi-knowledge-enhanced abstractive summarization model tailored for Korean texts. The model integrates internal knowledge, specifically keywords and topics that are extracted using a context-aware BERT-based approach. Unlike traditional statistical extraction methods, our approach utilizes the semantic context to ensure that the internal knowledge is both diverse and representative. By employing a multi-head attention mechanism, the proposed model effectively integrates multiple types of internal knowledge with the original document embeddings. Experimental evaluations on Korean datasets (news and legal texts) demonstrate that our model significantly outperforms baseline methods, achieving notable improvements in lexical overlap, semantic consistency, and structural coherence, as evidenced by higher ROUGE and BERTScore metrics. Furthermore, the method maintains information consistency across diverse categories, including dates, quantities, and organizational details. These findings highlight the potential of context-aware multi-knowledge integration in enhancing Korean abstractive summarization and suggest promising directions for future research into broader knowledge-incorporation strategies.