计算机科学 ›› 2020, Vol. 47 ›› Issue (2): 195-200.doi: 10.11896/jsjkx.181202410
张宜飞,王中卿,王红玲
ZHANG Yi-fei,WANG Zhong-qing,WANG Hong-ling
摘要： 商品评论摘要是从一个商品的所有评论中抽取出一系列有序的能够代表评论广泛意见的句子作为该商品的综合评论。篇章层次结构分析旨在对篇章内部各个语义单元之间的层次结构和语义关系进行分析。由此可见,分析篇章层次结构有利于更加准确地判断篇章内各个语义单元的语义信息和重要程度,这对于抽取篇章的重要内容有很大帮助。因此,文中提出了一种基于篇章层次结构的商品评论摘要方法。该方法基于LSTM(Long Short Term Memory Network)神经网络构建抽取式商品评论摘要模型,并利用注意力机制将篇章层次结构信息作为判断篇章单元重要程度的参照加入该模型中,以便更加准确地抽取出商品评论中的重要内容,从而提升整个任务的性能。将所提方法在Yelp 2013数据集上进行实验,并在ROUGE评价指标上进行评测。实验结果表明,加入篇章层次结构信息后,模型的ROUGE-1值达到了0.3608,与仅考虑评论句子信息的标准LSTM方法相比提升了1.57%,这说明在商品评论摘要任务中引入篇章层次结构信息能够有效地提升该任务的性能。
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