PSP: Pre-trained Soft Prompts for Few-Shot Abstractive Summarization

Xiaochen Liu, Yang Gao, Yu Bai, Jiawei Li, Yinan Hu, Heyan Huang, Boxing Chen


Abstract
Few-shot abstractive summarization has become a challenging task in natural language generation. To support it, we developed a novel soft prompts architecture coupled with a prompt pre-training plus prompt fine-tuning paradigm, which is effective and tunes only extremely light parameters. To meet the structure of the generation models, the soft prompts comprise continuous input embeddings across an encoder and a decoder. Importantly, a new inner-prompt placed in the text is introduced to capture document-level information. The aim is to devote attention to understanding the document that better prompts the model to generate document-related content. In the training process, the prompt pre-training with self-supervised pseudo-data firstly teaches the model basic summarizing capability. Then, with few-shot examples, only the designed lightweight soft prompts are fine-tuned. Experimental results on the CNN/DailyMail and XSum datasets show that our method, with only 0.1% of the parameters, outperforms full-model tuning where all model parameters are tuned. It also surpasses Prompt Tuning by a large margin and delivers competitive results against Prefix-Tuning with 3% of the parameters.
Anthology ID:
2022.coling-1.553
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6355–6368
Language:
URL:
https://aclanthology.org/2022.coling-1.553
DOI:
Bibkey:
Cite (ACL):
Xiaochen Liu, Yang Gao, Yu Bai, Jiawei Li, Yinan Hu, Heyan Huang, and Boxing Chen. 2022. PSP: Pre-trained Soft Prompts for Few-Shot Abstractive Summarization. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6355–6368, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
PSP: Pre-trained Soft Prompts for Few-Shot Abstractive Summarization (Liu et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.553.pdf