PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization
Xiao Wen, Iz Beltagy, Giuseppe Carenini, Arman Cohan · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2022
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.1