Suyoung Min  Youngjoong Ko

10.5626/JOK.2024.51.11.1011

dialogue summarization Transformer Cross-attention topic distribution

Unlike general document summarization, dialogue summarization frequently involves informal and colloquial language. It requires an understanding of the context and flow of the dialogue. It also requires consideration of topics. This study proposes a Topic-Aware Cross-Attention mechanism that can incorporate elements to recognize topic distributions into a cross-attention mechanism to reflect characteristics of dialogue. This Topic-Aware Cross-Attention mechanism can extract topic distributions of dialogue and summary and apply the similarity of these distributions to the cross-attention mechanism within BART model’s decoder to perform dialogue summarization. The proposed Topic-Aware Cross-Attention mechanism can adjust application degree of topic distribution similarity to the cross-attention mechanism by modifying topic-ratio. Experimental results on DialogSum and SAMSum datasets demonstrated the suitability of the method for dialogue summarization.