Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Domain-Oriented Prefix-Tuning: Towards Efficient and Generalizable
  Fine-tuning for Zero-Shot Dialogue Summarization   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 methods for domain adaptation of dialogue
summarization, in this paper, we propose an efficient and generalizable
Domain-Oriented Prefix-tuning model, which utilizes a domain word initialized
prefix module to alleviate domain entanglement and adopts discrete prompts to
guide the model to focus on key contents of dialogues and enhance model
generalization. We conduct zero-shot experiments and build domain adaptation
benchmarks on two multi-domain dialogue summarization datasets, TODSum and
QMSum. Adequate experiments and qualitative analysis prove the effectiveness of
our methods.

8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
16
The categories of this paper are: stat
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys