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

Abstract

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%, 645% 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.

Keywords

• natural language processing; khmer; extractive summarization; deep learning; waterfall method; maximal marginal relevance(mmr)