Peringkasan Teks Otomatis Menggunakan Metode Maximum Marginal Relevance Pada Hasil Pencarian Sistem Temu Kembali Informasi Untuk Artikel Berbahasa Indonesia - Nirmala Fa'izah Saraswati; Indriati Indriati; Rizal Setya Perdana (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
Information retrieval is a system that displays documents according to the query given by user. However, the information retrieval system provide a lot of search results, when we are looking for a desired information is not possible to open one by one documents generated by system. Text Summarization can be done to get an overview of information from a document, so that user get the right documents. One method to summarize text is Maximum Marginal Relevance (MMR). Maximum Marginal Relevance (MMR) is one of the extractive summary methods used to summarize single or multi document documents. MMR summarizes documents by computing the similarity between sentences and sentences, and between sentences and queries. Based on the test results, it obtain best Precision at k in the fifth rank of 0.96 for information retrieval system results. The best test results from an average precision, recall, f-measure and accuracy respectively 0.70, 0.75, 0.70 and 74.17. The used method is good enough to get the relevant documents and obtain summaries based on the title corresponding to the contents of the document.