论文标题
主题指导的抽象文本摘要：一种联合学习方法
Topic-Guided Abstractive Text Summarization: a Joint Learning Approach
论文作者
论文摘要
我们介绍了一种新的方法，用于抽象文本摘要，主题指导的抽象摘要，该方法从具有全球显着内容的主题级特征校准了远程依赖性。这个想法是将神经主题建模与基于变压器的顺序到序列（SEQ2SEQ）模型合并到联合学习框架中。该设计可以学习和保留文档的全球语义，该语言可以为捕获文档的重要想法提供其他上下文指南，从而增强摘要的产生。我们在两个数据集上进行了广泛的实验，结果表明，根据胭脂测量和人类评估，我们提出的模型的表现优于许多提取和抽象系统。我们的代码可在以下网址提供：https：//github.com/chz816/tas。
We introduce a new approach for abstractive text summarization, Topic-Guided Abstractive Summarization, which calibrates long-range dependencies from topic-level features with globally salient content. The idea is to incorporate neural topic modeling with a Transformer-based sequence-to-sequence (seq2seq) model in a joint learning framework. This design can learn and preserve the global semantics of the document, which can provide additional contextual guidance for capturing important ideas of the document, thereby enhancing the generation of summary. We conduct extensive experiments on two datasets and the results show that our proposed model outperforms many extractive and abstractive systems in terms of both ROUGE measurements and human evaluation. Our code is available at: https://github.com/chz816/tas.
