Introduces listwise attention, listwise loss, and GBDT predictor to improve multimodal review helpfulness ranking over prior FCNN and pairwise approaches.
Enriching and Controlling Global Semantics for Text Summarization
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abstract
Recently, Transformer-based models have been proven effective in the abstractive summarization task by creating fluent and informative summaries. Nevertheless, these models still suffer from the short-range dependency problem, causing them to produce summaries that miss the key points of document. In this paper, we attempt to address this issue by introducing a neural topic model empowered with normalizing flow to capture the global semantics of the document, which are then integrated into the summarization model. In addition, to avoid the overwhelming effect of global semantics on contextualized representation, we introduce a mechanism to control the amount of global semantics supplied to the text generation module. Our method outperforms state-of-the-art summarization models on five common text summarization datasets, namely CNN/DailyMail, XSum, Reddit TIFU, arXiv, and PubMed.
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UNVERDICTED 2representative citing papers
Multimodal contrastive learning with adaptive weighting and interaction module achieves state-of-the-art results on two MRHP benchmark datasets.
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Introduces listwise attention, listwise loss, and GBDT predictor to improve multimodal review helpfulness ranking over prior FCNN and pairwise approaches.
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