Dialogue summarization enhanced response generation for multi-domain task-oriented dialogue systems

Task -oriented dialogue systems (TOD) are blossoming with the advances in pre -trained language models (PrLM). Recently, research on PrLM-based multi -domain TOD has arisen with many outstanding outcomes. However, three challenges still need to be thoroughly studied. First, most current works regard dialogue state tracking as a generative problem supervised by concatenated slot -value sequences, impairing the models' domain adaption because of the discrepancy between PrLM's natural text inputs and spliced slot -value spans. Second, most existing works seldom specifically consider how to deal with long and involved dialogue history caused by multiple task domains. Third, few studies are concerned with enhancing the model's reasoning ability to handle intricate contexts. To alleviate these issues, we propose a dialogue summarization enhanced response generation framework for multi -domain TOD. Specifically, we offer a novel summarization model that employs the query and the generated summarization from the previous turn to obtain beneficial information for the current turn, which is then combined with the entire dialogue history to produce the final summary. Then, the generated dialogue summarization is fed to the response decoder as dialogue states and key dialogue histories through the designed dynamic fusion mechanism to yield responses. Experimental results indicate that the proposed model for response generation task outperforms the baseline models in both automatic and human evaluations on two public datasets.