A Knowledge Graph-Based Abstractive Model Integrating Semantic and Structural Information for Summarizing Chinese Meetings

With the rapid increase of users, online meeting platforms have accumulated massive meeting transcripts. However, it is still a challenge for users to quickly master the chief information and manage the meetings, despite there are already some useful text summarization models. In this paper, a Knowledge Graph-based Meeting Summarization Framework is proposed to tackle this challenge. First, a two-layers meeting domain Knowledge Graph is developed to integrate more information of meetings. Based on which, an encoder-decoder architecture is utilized to summarize meetings. For encoding meetings, a structural-level and semantic-level embedding strategy is considered, concretely, the Knowledge Graph is embedded to obtain the structural information, an interaction intention recognition model and a two-level transformer mechanism are devised to get the semantic information. Finally, the structural information and semantic information are combined and fed into the decoding network to generate meeting summaries. Extensive experiments on the Chinese meeting dataset show that our summarization framework outperforms other state-of-the-art models.