A Dialogues Summarization Algorithm Based on Multi-task Learning

With the continuous advancement of social information, the number of texts in the form of dialogue between individuals has exponentially increased. However, it is very challenging to review the previous dialogue content before initiating a new conversation. In view of the above background, a new dialogue summarization algorithm based on multi-task learning is first proposed in the paper. Specifically, Minimum Risk Training is used as the loss function to alleviate the problem of inconsistent goals between the training phase and the testing phase. Then, in order to deal with the problem that the model cannot effectively distinguish gender pronouns, a gender pronoun discrimination auxiliary task based on contrast learning is designed to help the model learn to distinguish different gender pronouns. Finally, an auxiliary task of reducing exposure bias is introduced, which involves incorporating the summary generated during inference into another round of training to reduce the difference between the decoder inputs during the training and testing stages. Experimental results show that our model outperforms strong baselines on three public dialogue summarization datasets: SAMSUM, DialogSum, and CSDS.