Indonesian News Extractive Text Summarization Using Latent Semantic Analysis
Rizka Ainur Rofiq, Suyanto
Informasi Umum
Kode
23.09.006
Klasifikasi
006.312 - Data mining
Jenis
Artikel - Restricted Use
Subjek
Text Mining, Data Mining,
No. Rak
Dilihat
197 kali
Informasi Lainnya
Abstraksi
News text is a text that contains important information that is happening to be disseminated to the public. In the news, the more information, the more text is displayed. Of course, it takes a lot of time to read the entire text of the news. Automatic text summarization is needed to help readers understand the content of the news text quickly. In this study, the application of the latent semantic analysis method with the GongLiu, Steinberger Jezek, and Cross techniques will be applied to automatic text summarization. The test data will be tested by using local news about politics. By comparing the rate the three methods previously mentioned, Gongliu is considered the best amongst the three methods since it has the highest Rogue value and the fastest processing time. Keywords: text summarization, semantic analysis, comparing rate
Koleksi & Sirkulasi
Tersedia 1 dari total 1 Koleksi
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Pengarang
| Nama | Rizka Ainur Rofiq, Suyanto |
| Jenis | Perorangan |
| Penyunting | |
| Penerjemah |
Penerbit
| Nama | IC2SE |
| Kota | Bandung |
| Tahun | 2021 |
Sirkulasi
| Harga sewa | IDR 0,00 |
| Denda harian | IDR 0,00 |
| Jenis | Non-Sirkulasi |
