Peringkasan Literatur Ilmu Komputer Bahasa Indonesia Berbasis Fitur Statistik dan Linguistik menggunakan Metode Gaussian Naive Bayes - Muhammad Fhadli; Mochammad Ali Fauzi; Tri Afirianto (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2017)
In this era which require big amount of data, text summarization becomes a needs. With text summarization, everyone can get information that describe all of big text in just few of sentences. The problem in text summarization is quality of the summarization result. One of the known method for text summarization is TFIDF, this method is a method for summarizing text using statistical approach. The other approach for summarizing text is statistical approach. In a general way, summarization result is consist of sentences with statistical features such as total of words, total of keywords, and sentence position in the original text. Those features can be used to classify a text into class of summary or class of non summary. The summarization result come from the composite of every sentence in summary class. In this research, writer combines the use of statistical feature and linguistical features to summarize text. The testing result of this research show that summarization with statistical and linguistical features using Naive Bayes method came with f-score average 0.206538 and realive utility average 0.116657.