Overwhelming amounts of dialogue are being recorded in instant messaging apps, Slack channels, customer service interactions, and so on. Wouldn't it be great if automated tools could provide us short, succinct summaries?
However, challenges such as insufficient training data and low information density impede our ability to train abstractive summarization models. In this work, we propose a novel curriculum-based prompt learning method with self-training to address these problems. Specifically, prompts are learned using a curriculum learning strategy that gradually increases the degree of prompt perturbation, thereby improving the dialogue understanding and modeling capabilities of our model. Unlabeled dialogue is incorporated by means of self-training so as to reduce the dependency on labeled data. We further investigate topic-aware prompts to better plan for the generation of summaries. Experiments confirm that our model substantially outperforms strong baselines and achieves new state-of-the-art results on the AMI and ICSI datasets.
Published at EMNLP 2022
Paper: http://gerard.demelo.org/papers/dialogue-summarization.pdf
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