基于分层最大边缘相关的柬语多文档抽取式摘要方法

Khmer multi-document extractive summarization method based on hierarchical maximal marginal relevance

为了解决传统多文档抽取式摘要方法无法有效利用文档之间的语义信息、摘要结果存在过多冗余内容的问题,提出了一种基于分层最大边缘相关的柬语多文档抽取式摘要方法。首先,将柬语多文档文本输入到训练好的深度学习模型中,抽取得到所有的单文档摘要;然后,依据类似分层瀑布的方式,迭代合并所有的单文档摘要,通过改进的最大边缘相关算法合理地选择摘要句,得到最终的多文档摘要。结果表明,与其他方法相比,通过使用深度学习方法并结合分层最大边缘相关算法共同获得的柬语多文档摘要,R1,R2,R3和RL值分别提高了4.31%,5.33%,6.45%和4.26%。基于分层最大边缘相关的柬语多文档抽取式摘要方法在保证摘要句子多样性和差异性的同时,有效提高了柬语多文档摘要的质量。

In order to solve the problem of ineffective utilization of the semantic information between documents in the traditional multi-document extractive summarization method and the excessive redundant content in the summary result,a Khmer multi-document extractive summarization method based on hierarchical maximal marginal relevance(MMR)was proposed.Firstly,the Khmer multi-document text was input into the trained deep learning model to extract all the single-document summaries.Then,all single document summaries were iteratively merged according to a similar hierarchical waterfall method,and the improved MMR algorithm was used to reasonably select summary sentences to obtain the final multi-document summary.The experimental results show that the R1,R2,R3,RL values of the Khmer multi-document summary obtained by using the deep learning method combined with the hierarchical MMR algorithm increases by 4.31%,5.33%,6.45%and 4.26%respectively compared with other methods.The Khmer multi-document extractive summarization method based on hierarchical MMR can effectively improve the quality of Khmer multi-document summary while ensuring the diversity and difference of the summary sentences.