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An Unsupervised Masking Objective for Abstractive Multi-Document News Summarization

7 January 2022
Nikolai Vogler
Songlin Li
Yujie Xu
Yujian Mi
Taylor Berg-Kirkpatrick
    AAML
ArXiv (abs)PDFHTML
Abstract

We show that a simple unsupervised masking objective can approach near supervised performance on abstractive multi-document news summarization. Our method trains a state-of-the-art neural summarization model to predict the masked out source document with highest lexical centrality relative to the multi-document group. In experiments on the Multi-News dataset, our masked training objective yields a system that outperforms past unsupervised methods and, in human evaluation, surpasses the best supervised method without requiring access to any ground-truth summaries. Further, we evaluate how different measures of lexical centrality, inspired by past work on extractive summarization, affect final performance.

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