期刊名称：IAENG International Journal of Computer Science
印刷版ISSN：1819-656X
电子版ISSN：1819-9224
出版年度：2019
卷号：46
期号：3
页码：494-505
出版社：IAENG - International Association of Engineers
摘要：This paper presents a new unsupervised algorithm for determining extractive summary for a single document using term frequency prediction, which is obtained from memory-based collaborative filtering (CF) approach, and Mean Shift Clustering algorithm. The new algorithm uses Term-Sentence Collaborative Filtering (TSCF) for predicting term frequency. These term frequencies are used in sentence ranking according to the presence percentage of each word/term in each sentence. TSCF computes term frequencies for either terms present or missing (sparse) in a sentence via collaborative filtering prediction algorithm. The new algorithm uses Mean Shift Clustering algorithm as a final framework to group sentences according to their ranks to get more coherent summaries. Experiments show the effect of using different weighting functions including: Term Frequency (TF), Term Frequency Inverse Document Frequency (TFIDF) and binary TF. In addition, they show the effect of using different distance metrics that support sparse matrices representations including: Cosine, Euclidean and Manhattan. Experiments also, show the effect of using L1 and L2 normalization. ROUGE is used as a fully automatic metric in text summarization on DUC2002 datasets. Results show ROUGE-1, ROUGE-2, ROUGE-L and ROUGE-SU4 average recall, precision and f-measure scores, which show the effectiveness of the new algorithm. Results show that the proposed TSCF algorithm has promising results and outperforms related baseline techniques in many ROUGE scores.
关键词：Extractive Text Summarization; Collaborative Filtering Prediction; Term frequency; Information retrieval; Mean Shift Clustering
