Automatic Text Summarization are sorting paragraphs into shorter forms using a computer operated\u00a0applications. This automatic summarization technique works in a computer to summarize the text inputted by\u00a0user. A document entered into the computer application, then processed and summarized to produce a summary\u00a0from original text. In research document extraction method used Maximum Marginal Relevance algorithm.\u00a0MMR is a summary document extraction method is used to summarize a single document or multiple\u00a0documents. MMR summarizing document by calculating the similarity between the sentences text in a\u00a0paragraph. In this summarization contents for document segmentation process is carried out using a combination\u00a0of gender-based matrix cosine similarity. Cosine similarity is used to calculate the relevance of the query\u00a0approach on a document. The determination of relevance in a query against document is considered as\u00a0measurement of similarity between queries with vectors of documents. The results from the automatic summary\u00a0application is a sequential list words according to the results obtained algorithms. The closeness results with\u00a0query assessed from the lambda limit values applied in the cosine similarity is 0 to 1.<\/p>\n
Dibuat oleh :\u00a0Jasman Pardede,\u00a0Jordy Sinatria
\nE-mail :\u00a0jasman@itenas.ac.id
\nKata kunci :summarization, MMR, queries, similarity, cosine similarity.<\/p>\n
Keterangan : Karya Ilmiah ini\u00a0 dimuat pada Prosiding Konferensi Nasional Sistem Informasi KNSI 2015, 26 – 28 Februari 2015<\/p>\n