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arXiv · 2311.09836

PELMS: Pre-training for Effective Low-Shot Multi-Document Summarization

Joseph J. Peper·Wenzhao Qiu·Lu Wang
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Abstract

We investigate pre-training techniques for abstractive multi-document summarization (MDS), which is much less studied than summarizing single documents. Though recent work has demonstrated the effectiveness of highlighting information salience for pre-training strategy design, it struggles to generate abstractive and reflective summaries, which are critical properties for MDS. To this end, we present PELMS, a pre-trained model that uses objectives based on semantic coherence heuristics and faithfulness constraints with un-labeled multi-document inputs, to promote the generation of concise, fluent, and faithful summaries. To support the training of PELMS, we compile MultiPT, a multi-document pre-training corpus containing over 93 million documents to form more than 3 million unlabeled topic-centric document clusters, covering diverse genres such as product reviews, news, and general knowledge. We perform extensive evaluation of PELMS in low-shot settings on a wide range of MDS datasets. Our approach consistently outperforms competitive comparisons with respect to overall informativeness, abstractiveness, coherence, and faithfulness.

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Joseph J. Peper, Wenzhao Qiu, Lu Wang. 2023-11-16. PELMS: Pre-training for Effective Low-Shot Multi-Document Summarization. https://arxiv.org/abs/2311.09836

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