Dialogue summarization aims to condense the original dialogue into a shorter version
covering salient information, which is a crucial way to reduce dialogue data overload …

Dialogue is an essential part of human communication and cooperation. Existing research
mainly focuses on short dialogue scenarios in a one-on-one fashion. However, multi-person …

Abstractive dialogue summarization generates a concise and fluent summary covering the
salient information in a dialogue among two or more interlocutors. It has attracted significant …

Dialogue summarization is an important task that requires to generate highlights for a
conversation from different aspects (eg, content of various speakers). While several studies …

With the rapid increase in the volume of dialogue data from daily life, there is a growing
demand for dialogue summarization. Unfortunately, training a large summarization model is …

Dialogue topic segmentation is a challenging task in which dialogues are split into segments
with pre-defined topics. Existing works on topic segmentation adopt a two-stage paradigm …

Role-oriented dialogue summarization is to generate summaries for different roles in the
dialogue, eg, merchants and consumers. Existing methods handle this task by summarizing …

Large language models (LLMs) like LLaMA, Baichuan and Bloom models show remarkable
ability with instruction fine-tuning in many natural language tasks. Nevertheless, for the …

Previous dialogue summarization datasets mainly focus on open-domain chitchat dialogues,
while summarization datasets for the broadly used task-oriented dialogue haven't been …

The most advanced abstractive dialogue summarizers lack generalization ability on new
domains and the existing researches for domain adaptation in summarization generally rely …