Improving Role-Oriented Dialogue Summarization with Interaction-Aware Contrastive Learning (2024.lrec-main)
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| Challenge: | Existing methods for encoding dialogues do not capture interaction information between roles, thus ignore interaction-related key information. |
| Approach: | They propose a contrastive learning based interaction-aware model for the role-oriented dialogue summarization namely CIAM and use it to train the decoder to learn role-level interaction. |
| Outcome: | The proposed model captures interaction information between different roles and produces informative summaries on two public datasets. |
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