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

• URL: http://arxiv.org/abs/2110.08499v1
• Date: Sat, 16 Oct 2021 07:22:24 GMT
• Title: PRIMER: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization
• Authors: Wen Xiao, Iz Beltagy, Giuseppe Carenini, Arman Cohan
• Abstract summary: We propose PRIMER, a pre-trained model for multi-document representation with focus on summarization. Specifically, we adopt the Longformer architecture with proper input transformation and global attention to fit for multi-document inputs. Our model, PRIMER, outperforms current state-of-the-art models on most of these settings with large margins.
• Score: 16.830963601598242
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently proposed pre-trained generation models achieve strong performance on single-document summarization benchmarks. However, most of them are pre-trained with general-purpose objectives and mainly aim to process single document inputs. In this paper, we propose PRIMER, a pre-trained model for multi-document representation with focus on summarization that reduces the need for dataset-specific architectures and large amounts of fine-tuning labeled data. Specifically, we adopt the Longformer architecture with proper input transformation and global attention to fit for multi-document inputs, and we use Gap Sentence Generation objective with a new strategy to select salient sentences for the whole cluster, called Entity Pyramid, to teach the model to select and aggregate information across a cluster of related documents. With extensive experiments on 6 multi-document summarization datasets from 3 different domains on the zero-shot, few-shot, and full-supervised settings, our model, PRIMER, outperforms current state-of-the-art models on most of these settings with large margins. Code and pre-trained models are released at https://github.com/allenai/PRIMER

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