基于改进的MMR算法的新闻文本抽取式摘要方法

doi:10.3969/j.issn.0255-8297.2021.03.010

Abstract

Abstract: This paper proposes a news extraction method based on maximal marginal relevance (MMR) and a news extraction method based on support vector machine and maximal marginal relevance (SVM-MMR). The first method improves the traditional MMR news extraction method, and the second one uses the improved MMR news extraction method to make a second choice of the SVM classification results. Compared with the traditional MMR news extraction method, the average precision of MMR-based and SVMMMR-based news extraction methods are improved by 0.148 and 0.204, respectively. And the extraction efficiency of the MMR-based method is about 3 times of that of the SVMMMR method. The augmented MMR algorithm is more suitable for application scenarios that require high summarization efficiency, especially for long text summarization, while the SVM-MMR method is more suitable for generating a more comprehensive summary of the text content.

Key words: news extraction, extractive summarization, redundant processing, support vector machine (SVM), maximal marginal relevance (MMR)

CLC Number:

TP391

CHENG Kun, LI Chuanyi, JIA Xinxin, GE Jidong, LUO Bin. News Summarization Extracting Method Based on Improved MMR Algorithm[J]. Journal of Applied Sciences, 2021, 39(3): 443-442.

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News Summarization Extracting Method Based on Improved MMR Algorithm

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