Steady progress has been made in abstractive summarization with attention-based sequence-to-sequence learning models. In this paper, we propose a new decoder where the output summary is generated by conditioning on both the input text and the latent topics of the document. The latent topics, identified by a topic model such as LDA, reveals more global semantic information that can be used to bias the decoder to generate words. In particular, they enable the decoder to have access to additional word co-occurrence statistics captured at document corpus level. We empirically validate the advantage of the proposed approach on both the CNN/Daily Mail and the WikiHow datasets. Concretely, we attain strongly improved ROUGE scores when compared to state-of-the-art models.
翻译:在抽象的总结和基于关注的顺序到顺序的学习模式方面,取得了稳步的进展。在本文件中,我们提出了一个新的解码器,产出摘要是根据文件输入文本和潜在专题的附加条件产生的。由LDA等专题模型确定的潜在专题揭示了更多的全球语义信息,可以用来偏向解码器生成单词。特别是,它们使解码器能够获取在文件文库一级捕捉到的更多单词共发统计数据。我们通过经验验证了CNN/Daily Mail和WikiHow数据集的拟议方法的优势。具体地说,我们大大改进了ROUGE的评分,与最先进的模型相比。