Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization

Understand

Current dialogue summarization systems usually encode the text with a number of general semantic features (e.g., keywords and topics) to gain more powerful dialogue modeling capabilities.

• However, these features are obtained via open-domain toolkits that are dialog-agnostic or heavily relied on human annotations.
• In this paper, we show how DialoGPT, a pre-trained model for conversational response generation, can be developed as an unsupervised dialogue annotator, which takes advantage of dialogue background knowledge encoded in DialoGPT.
• We apply DialoGPT to label three types of features on two dialogue summarization datasets, SAMSum and AMI, and employ pre-trained and non pre-trained models as our summarizes.