Abstractive dialogue summarization is the task of distilling conversations into informative
and concise summaries. Although focused reviews have been conducted on this topic, there …

This paper focuses on answering subjective questions about products. Different from the
factoid question with a single answer span, this subjective one involves multiple viewpoints …

Employing pre-trained Large Language Models (LLMs) has become the de facto standard in
Natural Language Processing (NLP) despite their extensive data requirements. Motivated by …

Dialogue summarization aims to distill a given conversation into a brief and focused
summary. The challenge lies in the diverse perspectives of participants and the frequent …

Pre-trained conversation models (PCMs) have demonstrated remarkable results in task-
oriented dialogue (TOD) systems. Many PCMs focus predominantly on dialogue …

Advertising systems often face the multi-domain challenge, where data distributions vary
significantly across scenarios. Existing domain adaptation methods primarily focus on …

Recently, LoRA has emerged as a crucial technique for fine-tuning large pre-trained models,
yet its performance in multi-task learning scenarios often falls short. In contrast, the MoE …

Most Fake News Detection (FND) methods often struggle with data scarcity for emerging
news domain. Recently, prompt learning based on Pre-trained Language Models (PLM) has …

Recent advances in prompt optimization have notably enhanced the performance of pre-
trained language models (PLMs) on downstream tasks. However, the potential of optimized …

Abstract Domain adaptation from labeled source domains to the target domain is important
in practical summarization scenarios. However, the key challenge is domain knowledge …