UPER: Boosting Multi-Document Summarization with an Unsupervised Prompt-based Extractor
Shangqing Tu, Jifan Yu, Fangwei Zhu, Juanzi Li, Lei Hou, Jian-Yun Nie
Abstract
Multi-Document Summarization (MDS) commonly employs the 2-stage extract-then-abstract paradigm, which first extracts a relatively short meta-document, then feeds it into the deep neural networks to generate an abstract. Previous work usually takes the ROUGE score as the label for training a scoring model to evaluate source documents. However, the trained scoring model is prone to under-fitting for low-resource settings, as it relies on the training data. To extract documents effectively, we construct prompting templates that invoke the underlying knowledge in Pre-trained Language Model (PLM) to calculate the document and keyword’s perplexity, which can assess the document’s semantic salience. Our unsupervised approach can be applied as a plug-in to boost other metrics for evaluating a document’s salience, thus improving the subsequent abstract generation. We get positive results on 2 MDS datasets, 2 data settings, and 2 abstractive backbone models, showing our method’s effectiveness. Our code is available at https://github.com/THU-KEG/UPER- Anthology ID:
- 2022.coling-1.550
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 6315–6326
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.550
- DOI:
- Cite (ACL):
- Shangqing Tu, Jifan Yu, Fangwei Zhu, Juanzi Li, Lei Hou, and Jian-Yun Nie. 2022. UPER: Boosting Multi-Document Summarization with an Unsupervised Prompt-based Extractor. In Proceedings of the 29th International Conference on Computational Linguistics, pages 6315–6326, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- UPER: Boosting Multi-Document Summarization with an Unsupervised Prompt-based Extractor (Tu et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/2022.coling-1.550.pdf
- Code
- thu-keg/uper
- Data
- WCEP, WikiCatSum, WikiSum