摘要：

为提高中文文本摘要提取的准确度,融合Doc2Vec模型、K-means算法和TextRank算法,提出一种中文文本摘要自动提取算法(DK-TextRank)。使用Doc2Vec模型进行文本向量化,采用改进的K-means算法实现相似文本聚类,在每个聚类簇中应用加入权重影响因子的TextRank算法对文本语句进行排序,并提取主题句生成摘要。实验结果表明,DK-TextRank算法在摘要语句数量为7时F值达到79.36%,相比传统TF-IDF、TextRank算法提取的摘要质量更高。

关键词: Doc2Vec模型, K-means算法, TextRank算法, 摘要提取, 权重影响因子

Abstract:

This paper proposes a Chinese text summarization extraction algorithm,called DK-TextRank,combines Doc2Vec model,K-means and TextRank algorithm for Chinese texts to improve summarization accuracy.After using the Doc2Vec model for text vectorization,the DK-TextRank algorithm uses an improved K-means algorithm for similar text clustering,and the TextRank algorithm with weight impact factors in each cluster to sort and extract topic sentence.Then,it generates a summary.Experimental results show that,compared with traditional TF-IDF,TextRank algorithm,the DK-TextRank algorithm has an F value of 79.36% when the number of summary statements is 7,and the extracted abstract has higher quality.

Key words: Doc2Vec model, K-means algorithm, TextRank algorithm, summarization extraction, weight influence factor

中图分类号: 

• TP391