Indonesian Abstractive Text Summarization Using Bidirectional Gated Recurrent Unit

RIKE ADELIA

Informasi Dasar

19.04.3418
005.262
Karya Ilmiah - Skripsi (S1) - Reference

Abstractive text summarization is more challenging than the extractive one since it is performed by paraphrasing the entire contents of the text, which has a higher di culty. But, it produces a more natural summary and higher inter-sentence cohesion. Recurrent Neural Network (RNN) has experienced success in summarizing abstractive texts for English and Chinese texts. The Bidirectional Gated Recurrent Unit (BiGRU) RNN architecture is used so that the resulted summaries are influenced by the surrounding words. In this research, such a method is applied for Bahasa Indonesia to improve the text summarizations those are commonly developed using some extractive methods with low inter-sentence cohesion. An evaluation on a dataset of Indonesian journal documents shows that the proposed model is capable of summarizing the overall contents of testing documents into some summaries with high similarities to the provided abstracts. The proposed model resulting success in understanding source text for generating summarization.

Subjek

Natural language processing
 

Katalog

Indonesian Abstractive Text Summarization Using Bidirectional Gated Recurrent Unit
 
 
Indonesia

Sirkulasi

Rp. 0
Rp. 0
Tidak

Pengarang

RIKE ADELIA
Perorangan
Suyanto
 

Penerbit

Universitas Telkom, S1 Informatika
Bandung
2019

Koleksi

Kompetensi

  • CCH3F3 - KECERDASAN BUATAN
  • CSH3L3 - PEMBELAJARAN MESIN
  • CSH4O3 - PEMROSESAN BAHASA ALAMI
  • CSH4H3 - PENAMBANGAN TEKS
  • CCH4D4 - TUGAS AKHIR
  • CII3C3 - PEMBELAJARAN MESIN
  • CII4G3 - PEMROSESAN BAHASA ALAMI
  • CII4E4 - TUGAS AKHIR
  • CPI3C3 - PEMBELAJARAN MESIN
  • III4A4 - TUGAS AKHIR

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