Peringkasan Teks Otomatis Pada Artikel Berita Hiburan Berbahasa Indonesia Menggunakan Metode BM25 - Desy Andriani; Indriati Indriati; Muhammad Tanzil Furqon (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2019)
One of the most often activitiy carried out by Indonesian internet users is reading news. More than 50% of Indonesian internet users use the internet to read news. However, problems will arise if the content of the article is a long text so that the reader needs time to read and understand the contents of the article. One way that users can still read and understand the contents of articles quickly is by reading the summary. Therefore we need an automatic text summarization system in entertainment news articles with the aim of emphasizing the main information and helping the reader get the main information from the text quickly and don't need to read the entire contents of the text or document. This study uses the BM25 method which is a method of weighting sentences that sort sentences based on terms that appear in each sentence in the document. BM25 is using tf idf weighting for word weighting and the relationship between terms and each sentence in the document is influenced by free parameters k1 and b. Based on the test results it was found that summarizing the text with the BM25 method obtained the best average precision result, recall and f-measure values ​​when the value of the compression rate used was 30%. Where the average values ​​of precision, recall, and f-measure are 0,730, 0,738 and 0,734.