PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization

Wen Xiao, Iz Beltagy, Giuseppe Carenini, Arman Cohan


Abstract
We introduce PRIMERA, a pre-trained model for multi-document representation with a focus on summarization that reduces the need for dataset-specific architectures and large amounts of fine-tuning labeled data. PRIMERA uses our newly proposed pre-training objective designed to teach the model to connect and aggregate information across documents. It also uses efficient encoder-decoder transformers to simplify the processing of concatenated input documents. With extensive experiments on 6 multi-document summarization datasets from 3 different domains on zero-shot, few-shot and full-supervised settings, PRIMERA outperforms current state-of-the-art dataset-specific and pre-trained models on most of these settings with large margins.
Anthology ID:
2022.acl-long.360
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5245–5263
Language:
URL:
https://aclanthology.org/2022.acl-long.360
DOI:
10.18653/v1/2022.acl-long.360
Bibkey:
Cite (ACL):
Wen Xiao, Iz Beltagy, Giuseppe Carenini, and Arman Cohan. 2022. PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5245–5263, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization (Xiao et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.360.pdf
Software:
 2022.acl-long.360.software.zip
Code
 allenai/primer
Data
Multi-NewsNewSHeadWCEPWikiSumarXivarXiv Summarization Dataset