Search results

The purpose of this paper is to design and implement new tracking and summarization algorithms for Chinese news content. Based on the proposed methods and algorithms, the authors extract the important sentences that are contained in topic stories and list those sentences according to timestamp order to ensure ease of understanding and to visualize multiple news stories on a single screen.

This paper encompasses an investigational approach that implements a new Dynamic Centroid Summarization algorithm in addition to a Term Frequency (TF)-Density algorithm to empirically compute three target parameters, i.e., recall, precision, and F-measure.

The proposed TF-Density algorithm is implemented and compared with the well-known algorithms Term Frequency-Inverse Word Frequency (TF-IWF) and Term Frequency-Inverse Document Frequency (TF-IDF). Three test data sets are configured from Chinese news web sites for use during the investigation, and two important findings are obtained that help the authors provide more precision and efficiency when recognizing the important words in the text. First, the authors evaluate three topic tracking algorithms, i.e., TF-Density, TF-IDF, and TF-IWF, with the said target parameters and find that the recall, precision, and F-measure of the proposed TF-Density algorithm is better than those of the TF-IWF and TF-IDF algorithms. In the context of the second finding, the authors implement a blind test approach to obtain the results of topic summarizations and find that the proposed Dynamic Centroid Summarization process can more accurately select topic sentences than the LexRank process.

The results show that the tracking and summarization algorithms for news topics can provide more precise and convenient results for users tracking the news. The analysis and implications are limited to Chinese news content from Chinese news web sites such as Apple Library, UDN, and well-known portals like Yahoo and Google.

The research provides an empirical analysis of Chinese news content through the proposed TF-Density and Dynamic Centroid Summarization algorithms. It focusses on improving the means of summarizing a set of news stories to appear for browsing on a single screen and carries implications for innovative word measurements in practice.

The content of training (art-based method) and instructional strategies (blended learning) can improve business school students' creativity and attempts to determine how training can be maintained using longitudinal tracking. The study aims to answer (1) whether the incorporation of art-based methods enhances the creativity of students compared to traditional face-to-face (F2F) teaching, and (2) whether such creative training and blended teaching methods have a higher transfer of training.

This study adopted a two-stage design (1) it adopted a 2 × 2 (with or without art-based methods * blended teaching or F2F teaching) between-subject design of experiments with 221 participants and (2) a one-year follow-up study was conducted (participants who were employed for 6 months to one year after graduation) with 187 participants and their directors.

The results showed that the inclusion of art-based methods in the creative training of students strengthens creative ability of the students; there were no significant differences between blended and traditional learning. The authors examined the effect of transferring creative training through a questionnaire analysis of participants and employers of the participants. Self-regulated and self-directed learning positively influence motivation to transfer, which positively influences creative performance.

The higher the level of self-regulated and self-directed learning of students, the more effective the transfer of creative training is over time.