Incremental Rapidly Grouping Aggregation Method for Similar Web News Headline. (January 2020)

• Record Type: Journal Article
• Authors: Hu, Shengze; He, Chunhui; Zhang, Chong; Ge, Bin; Zhu, Huiming; Liu, Wei
• Is Part Of: Journal of physics. Volume 1453(2020)
• Publication Date: 2020-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1453/1/012153
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: With the popularity of the Internet, real-time web news has been extensively used in human daily life. Because of competition and repetitive reporting between the media, there may be multiple similar news reporting the same searing event, which may waste a lot of time for users to obtain and read the similar news. Hence, how to rapid aggregating similar news to help users obtain relevant news information accurately is a very meaningful and essential study. However, the accuracy of the existing aggregation method needs to be strengthened, and it not meet real-time incremental processing requirements. In this paper, rapid incremental aggregating a lot of similar news headlines are our main study targets. We proposed a novel Rapidly Grouping Aggregation (RGA) method. It carries out similar news headline aggregation via two core aspects --- Improved Jaro-Winkler (IJW) similarity calculation, normalized mapping and rapidly grouping aggregation. The IJW using hierarchical matching windows to enhance the performance of similarity calculation, and using the MD5 normalization strategy and the Group By function of the MongoDB database to carry out the similarity news headline rapidly grouping. Compared with the state-of-the-art method, the actual system application results show that the RGA method achieves higher performance on similar news headline aggregation tasks.