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- W2516898639 abstract "News summarization was interpreted as a text which is generated from one or more sentences that convey important information from news. One of the important phases in summarization is weighting the sentence (sentence scoring). In news summarization, sentence scoring methods is usually used as news features itselves. In the multiple issues, there is one issue which becomes the subject, it is called Trending Issue. Trending Issue should be considered in the summarization process to produce more coherent summary. This study aims to summarize multiple news using a new sentence scoring method based on the Trending Issue and some important features from news, there are word frequency, TF-IDF, sentence position, and resemblance sentence to the title (NEFTIS). In this research, Trending Issue is obtained from Twitter by classifying tweets and then each tweet’s groups are extracted to get the issue. Furthermore, giving the weight of each issue using the Cluster Importance (CI) method. The issue with the largest weight will be selected as the Trending Issue. There are five steps to produce a multi-news summary with NEFTIS, i.e. Trending Issue extraction, news retrieval, news extracting feature, the scoring sentence, and the generating summary. ROUGE-1 is used to measure the quality of method by analyzing the performance of summary result using NEFTIS, it is compared with the summary results using only news feature (NeFS). The average yield max-ROUGE-1 for a whole variety of the number of sentences (n) indicates that NEFTIS method is more accurate than NeFS with the average value of max-largest ROUGE-1 0.8201 for n = 30." @default.
- W2516898639 created "2016-09-16" @default.
- W2516898639 creator A5049227899 @default.
- W2516898639 date "2015-01-01" @default.
- W2516898639 modified "2023-09-23" @default.
- W2516898639 title "PEMBOBOTAN KALIMAT BERDASARKAN FITUR BERITA DAN TRENDING ISSUE UNTUK PERINGKASAN MULTI DOKUMEN BERITA" @default.
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- W2516898639 hasPublicationYear "2015" @default.
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