Pardede, Jasman and Sinatria, Jordy (2015) IMPLEMENTASI MAXIMUM MARGINAL RELEVANCE DAN MATRIKS COSINE SIMILARITY PADA APLIKASI PERINGKASAN DOKUMEN. In: KONFERENSI NASIONAL SISTEM INFORMASI 2015, 26-28 Februari 2015, Universitas Klabat, Sulawesi Utara.

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Text 20. Implementasi Maximum Marginal Relevance dan Matriks Cosine Similarity pada Aplikasi Peringkasan Dokumen_Sertfikat.pdf Download (3MB) | Preview

Abstract

Text Summarization are sorting paragraphs into shorter forms using a computer operated applications. This automatic summarization technique works in a computer to summarize the text inputted by user. A document entered into the computer application, then processed and summarized to produce a summary from original text. In research document extraction method used Maximum Marginal Relevance algorithm. MMR is a summary document extraction method is used to summarize a single document or multiple documents. MMR summarizing document by calculating the similarity between the sentences text in a paragraph. In this summarization contents for document segmentation process is carried out using a combination of gender-based matrix cosine similarity. Cosine similarity is used to calculate the relevance of the query approach on a document. The determination of relevance in a query against document is considered as measurement of similarity between queries with vectors of documents. The results from the automatic summary application is a sequential list words according to the results obtained algorithms. The closeness results with query assessed from the lambda limit values applied in the cosine similarity is 0 to 1.

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