结合层级注意力的抽取式新闻文本自动摘要

Extractive News Text Automatic Summarization Combined with Hierarchical Attention

由于抽取式摘要抽取句子有较强的人为判断主观性,不能准确客观评测出文章中实际每个句子对摘要的重要程度,以及每句话中每个词对句子重要程度的影响,从而影响了摘要的抽取质量。针对该问题,提出了一种结合层级注意力的抽取式新闻文本自动摘要方法。首先,该方法通过对英文新闻文本进行层级编码并依次加入词级注意力、句级注意力,得到结合层级注意力的文本表示。其次,通过神经网络构建动态打分函数并依次选择出打分函数中分值最高的候选句子作为摘要句。最后,抽取出英文新闻文本所对应的摘要。所提方法在CNN/Daily Mail、New York Times与Multi-News公共数据集上均进行了实验验证,实验结果表明所提方法的ROUGE评测值与目前最好的模型相比表现相当,ROUGE F1值较baseline分别提高了1.78、0.70与1.44个百分点。由此表明该方法在英文新闻文本抽取式摘要任务上具有泛化性与有效性,并且与现有方法相比具有一定的优越性。

Extractive summarization is of strong human subjectivity,it is therefore impossible to evaluate the importance of each sentence in the article and the influence of each word on the sentence,which would affect the quality of extractive summarization.In response to this problem,this paper proposes an automatic text summarization approach to news text combined with hierarchical attention.Firstly,this method uses hierarchical coding of English news text and adds word-level attention and sentence-level attention in turn to obtain a text representation combined with hierarchical attention.Secondly,a dynamic scoring function is constructed through the neural network and the candidate sentence with the highest score in the scoring function is selected in turn as the summary sentence.Finally,the summarization is extracted corresponding to the English news text.The proposed method is experimentally verified on public datasets of CNN/Daily Mail,New York Times and Multi-News.Experimental results show that the ROUGE evaluation value of the proposed method is equivalent to the current best model,and the ROUGE F1 value is increased by 1.78,0.70 and 1.44 percentage points respectively than the baseline,which shows that the method has generalization and effectiveness in the task of extracting English news texts,and it has certain advantages compared with the existing methods.