Enhancing Abstractive Dialogue Summarization with Internal Knowledge

The task of dialogue summarization involves distilling a given dialogue into a concise and coherent summary. However, discrepancies in language styles between dialogues and summaries, scattered crucial information, incomplete utterances with ellipsis, and coreferences bring unique challenges to dialogue summarization. To tackle these challenges, we present multiple strategies in this study to extract crucial information with varying levels of granularity in dialogue from word-level and utterance-level semantics. This crucial information is used as valuable annotations on the dialogue text to train the model to recognize and utilize key information during the training phase, and enhance the model’s ability to identify crucial information during inference to generate better dialogue summaries. Experimental results on the SAMSum and DialogSum datasets shows that our method outperforms strong baseline models in terms of both ROUGE and BERTScore metrics. We corroborate these improvements through human evaluation.