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arXiv · 2110.08499

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

Wen Xiao·Iz Beltagy·Giuseppe Carenini·Arman Cohan
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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. The code and pre-trained models can be found at \url{https://github.com/allenai/PRIMER}.

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Wen Xiao, Iz Beltagy, Giuseppe Carenini, Arman Cohan. 2021-10-16. PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization. https://arxiv.org/abs/2110.08499

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