Dialogue Discourse-Aware Graph Model and Data Augmentation for Meeting Summarization

Understand

Meeting summarization is a challenging task due to its dynamic interaction nature among multiple speakers and lack of sufficient training data.

• Existing methods view the meeting as a linear sequence of utterances while ignoring the diverse relations between each utterance.
• Besides, the limited labeled data further hinders the ability of data-hungry neural models.
• In this paper, we try to mitigate the above challenges by introducing dialogue-discourse relations.