Penerapan Algoritma Maximum Marginal Relevance Dalam Peringkasan Teks Secara Otomatis
-
Ade Kurniawan
*
Universitas Budi Darma, Medan,
Indonesia
- Mohd Irsan Humaidy Universitas Budi Darma, Medan, Indonesia (*) Corresponding Author
Abstract
The search engine is a device for searching information from available documents. But the search engine will give you a lot of search results, so to find a document we have to open a meeting. Text summarization allows us to get key information from a document quickly without requiring us to read the contents of the document manually.One of the methods used is Maximum Marginal Relevance (MMR). MMR belongs to the extractive summary category (choosing the sentence in the document as the main sentence of the document content). MMR is done by weighting each sentence in the document.It is hoped that with the application of the Maximum Marginal Relevance Method this automatic summary of text can reduce the level of data redundancy and can help readers understand the meaning of the summary results and provide good information
Downloads
References
M. Mustaqhfiri, Z. Abidin, and R. Kusumawati, “Peringkasan Teks Otomatis Berita Berbahasa Indonesia Menggunakan Metode Maximum Marginal Relevance,” Matics, 2012, doi: 10.18860/mat.v0i0.1578.
N. F. Saraswati, Indriati, and R. S. Perdana, “Peringkasan Teks Otomatis Menggunakan Metode Maximum Marginal Relevance Pada Hasil Pencarian Sistem Temu Kembali Informasi Untuk Artikel Berbahasa Indonesia,” J. Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 2, no. 11, pp. 5494–5502, 2018, doi: 10.1016/s1010-6030(01)00380-x.
E. B. Setiawan and A. T. Hartanto, “Implementasi Metode Maximum Marginal Relevance (MMR) dan Algoritma Steiner Tree untuk Menentukan Storyline Dokumen Berita,” Jurnal ULTIMATICS, vol. 8, no. 1. 2017, doi: 10.31937/ti.v8i1.499.
E. Hovy and C.-Y. Lin, “Automated text summarization and the SUMMARIST system,” p. 197, 1996, doi: 10.3115/1119089.1119121.
M. Inderjeet, “Summarization Evaluation: An Overview,” Pflege Z., vol. 62, no. 6, pp. 337–341, 2009.
F. T. Waruwu and R. Mandala, “Perbandingan Algoritma Knuth Morris Pratt dan Boyer Moore Dalam Pencocokan String Pada Aplikasi Kamus Bahasa Nias,” J. Ilm. INFOTEK, vol. 1, no. 1, pp. 36–43, 2016.
S. Andini, “Klasifikasi Dokumen Teks Menggunakan Algoritma Naive Bayes dengan Bahasa Pemprograman Java,” Teknol. Inf. Pendidik., vol. 6, no. 2, pp. 140–147, 2013, [Online]. Available: http://www.jurnal-tip.net/jurnal-resource/file/13-Vol6No2Sep2013-Silfia Andini.pdf.
S. Xie and Y. Liu, “Using corpus and knowledge-based similarity measure in Maximum Marginal Relevance for meeting summarization,” ICASSP, IEEE Int. Conf. Acoust. Speech Signal Process. - Proc., vol. 2, no. 2, pp. 4985–4988, 2008, doi: 10.1109/ICASSP.2008.4518777.
F. David, Grossman and phir, Information Retrieval: Algorithm and Heuristics. Kluwer Academic Publisher, 1998.
H. W. A. Kesuma, “Penerapan Metode TF-IDF dan Cosine Similarity dalam Aplikasi Kitab Undang-Undang Hukum Dagang.” 2016.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Penerapan Algoritma Maximum Marginal Relevance Dalam Peringkasan Teks Secara Otomatis
Pages: 49-56
Copyright (c) 2022 Ade Kurniawan, Mohd Irsan Humaidy
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).