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Gated dynamic convolutions with deep layer fusion for abstractive document summarization

机译：具有深层层融合的门控动态综合，用于抽象文件摘要

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摘要

We present a novel abstractive document summarization based on the recently proposed dynamic convolutional encoder-decoder architectures. We address several aspects of summarization that are not well modeled by the basic architecture, by integrating multiple layers of the encoder, controlling information flow in the hierarchy, and exploiting external knowledge. First, we propose a simple and efficient deep layer fusion to extract salient information from the encoder layers. Second, we propose a gating mechanism to control and maintain important contextual information through the encoder-decoder layers into dynamic convolutions. Lastly, we put part-of-speech information into the model as external knowledge to better predict filters for dynamic convolutions. We evaluate our model using ROUGE metrics on three different datasets: CNN-DM, NEWSROOM-ABS, and XSUM. Experimental results show that the proposed model outperforms the state-of-the-art abstractive models on NEWSROOM-ABS and XSUM and shows comparable scores on CNN-DM.
机译：我们提出了一种基于最近提出的动态卷积编码器 - 解码器架构的新型抽象文件摘要。我们通过集成了编码器的多个层，控制层次结构中的信息流并利用外部知识来解决基本架构的概要概括的几个方面。首先，我们提出了一种简单有效的深层融合，以从编码器层中提取显着信息。其次，我们提出了一种通过编码器解码器层来控制和维持重要的语境信息，进入动态卷积。最后，我们将语音信息作为外部知识放入模型中，以更好地预测动态卷曲的过滤器。我们在三个不同数据集上使用Rouge指标评估我们的模型：CNN-DM，新闻室 - ABS和XSUM。实验结果表明，该模型优于新闻室-ABS和XSUM上的最先进的抽象模型，并在CNN-DM上显示了可比的分数。

著录项

  • 来源
    《Computer speech and language》 |2021年第3期|101159.1-101159.14|共14页
  • 作者

    Hongseok Kwon; Byung-Hyun Go; Juhong Park; Wonkee Lee; Yewon Jeong; Jong-Hyeok Lee;

  • 作者单位

    Department of Computer Science and Engineering Pohang University of Science and Technology 77 Cheongam-ro Nam-gu Pohang 37673 Republic of Korea;

    Department of Computer Science and Engineering Pohang University of Science and Technology 77 Cheongam-ro Nam-gu Pohang 37673 Republic of Korea;

    Department of Computer Science and Engineering Pohang University of Science and Technology 77 Cheongam-ro Nam-gu Pohang 37673 Republic of Korea;

    Department of Computer Science and Engineering Pohang University of Science and Technology 77 Cheongam-ro Nam-gu Pohang 37673 Republic of Korea;

    Department of Computer Science and Engineering Pohang University of Science and Technology 77 Cheongam-ro Nam-gu Pohang 37673 Republic of Korea;

    Pohang University of Science and Technology Graduate School of Artificial Intelligence;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Document summarization; Gated dynamic convolutions; Deep layer fusion; Convolutional encoder-decoder; Text generation;

    机译：文件摘要;门控动态综合;深层融合;卷积编码器解码器;文本生成;

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