Traditional dialogue summarization models rely on a large-scale manually-labeled corpus, lacking generalization ability to new domains, and domain adaptation from a labeled source domain to an unlabeled target domain is important in practical …
The most advanced abstractive dialogue summarizers lack generalization ability on new domains and the existing researches for domain adaptation in summarization generally rely on large-scale pre-trainings. To explore the lightweight fine-tuning …
Although abstractive summarization models have achieved impressive results on document summarization tasks, their performance on dialogue modeling is much less satisfactory due to the crude and straight methods for dialogue encoding. To address this …
Abstractive dialogue summarization suffers from a lots of factual errors, which are due to scattered salient elements in the multi-speaker information interaction process. In this work, we design a heterogeneous semantic slot graph with a slot-level …
Recently, people have been beginning paying more attention to the abstractive dialogue summarization task. Since the information flows are exchanged between at least two interlocutors and key elements about a certain event are often spanned across …