A Hierarchical Encoding-Decoding Scheme for Abstractive Multi-document Summarization

• URL: http://arxiv.org/abs/2305.08503v5
• Date: Wed, 1 Nov 2023 09:18:34 GMT
• Title: A Hierarchical Encoding-Decoding Scheme for Abstractive Multi-document Summarization
• Authors: Chenhui Shen, Liying Cheng, Xuan-Phi Nguyen, Yang You, Lidong Bing
• Abstract summary: Pre-trained language models (PLMs) have achieved outstanding achievements in abstractive single-document summarization (SDS) We propose a new method to better utilize a PLM to facilitate multi-document interactions for the multi-document summarization (MDS) task. Our method outperforms its corresponding PLM backbone by up to 3 Rouge-L and is favored by humans.
• Score: 66.08074487429477
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pre-trained language models (PLMs) have achieved outstanding achievements in abstractive single-document summarization (SDS). However, such benefits may not fully extend to multi-document summarization (MDS), where the handling of cross-document information is more complex. Previous works either design new MDS architectures or apply PLMs bluntly with concatenated source documents as a reformulated SDS task. While the former does not utilize previous pre-training efforts and may not generalize well across different domains, the latter may not sufficiently attend to the intricate cross-document relationships unique to MDS tasks. Instead, we enforce hierarchy on both the encoder and decoder to better utilize a PLM to facilitate multi-document interactions for the MDS task. Across 10 MDS benchmarks from various domains, our method outperforms or is competitive with the previous best models, including those with additional MDS pre-training or with more parameters. It outperforms its corresponding PLM backbone by up to 3 Rouge-L and is favored by humans.

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