Query-Based Dialogue Summarization Using BART

Conversation summarisation is the transformation of long conversational texts into concise and accurate summaries, the importance of which lies in improving the user experience and information filtering. As an important natural language processing task, conversation summarisation can provide concise and accurate information and avoid repetition and redundancy. In the dialogue summarisation task, pre-trained language models can be used to summarise long conversations and generate concise and accurate summaries. The aim of this paper is to investigate the possibility of using bidirectional and auto-regressive transformer models for dialogue summarisation tasks. In our experiments, we analysed the characteristics of the Query-based Multi-domain Meeting Summarization (QMsum) dialogue summarisation dataset, proposed a dialogue summarisation model based on the Bidirectional and Auto-Regressive Transformer model, and designed evaluation experiments to compare its performance with other methods in the dialogue summarisation task. The experimental results show that the results of this thesis are important for facilitating the development of dialogue summarisation tasks and the application of the Bidirectional and Auto-Regressive Transformer model.