Multi-Document Summarization Using K-Means and Latent Dirichlet Allocation (LDA) – Significance Sentences

Author:

Twinandilla Shiva,Adhy Satriyo,Surarso Bayu,Kusumaningrum Retno

Publisher

Elsevier BV

Subject

General Engineering

Reference20 articles.

1. Maharaj K. Technology and the Fake News Phenomenon. In Jamaica Conference Centre; 2017 Aug; Jamaica.

2. Chang YL, Chien JT. Latent Dirichlet Learning for Document Summarization. In Proceeding of 2009 IEEE International Conference on Acoustics, Speech and Signal Processing; 2009; Taipe, Taiwan. p. 1689-1692.

3. Sentence Scoring based on News Features and Trending Issues for Multi-Document Summarization;Hayatin;Jurnal Ilmiah Teknologi Informasi,2015

4. Irawan S, Hermawan, Samsuryadi. Preliminary Study of Indonesia Document Summarization using Latent Semantic Analysis and Maximum Marginal Relevance. In Proceeding of Annual Research Seminar (ARS); 2016; Palembang, Indonesia. p. 235–239.

5. Summarizing Text for Indonesian Language by Using Latent Dirichlet Allocation and Genetic Algorithm;Silvia,2014

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