Enhancing Indonesian text summarization with latent dirichlet allocation and maximum marginal relevance

Faisal, Muhammad ORCID: https://orcid.org/0000-0003-4884-7254, Mawaridi, Bima Hamdani, Afrah, Ashri Shabrina, Supriyono, Supriyono ORCID: https://orcid.org/0000-0002-4733-9189, Arif, Yunifa Miftachul ORCID: https://orcid.org/0000-0002-2183-0762, Aziz, Abdul, Wijayanti, Linda and Mulyadi, Melisa (2024) Enhancing Indonesian text summarization with latent dirichlet allocation and maximum marginal relevance. International Journal of Advanced Computer Science and Applications (IJACSA), 15 (8). pp. 519-528. ISSN 2156-5570

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Abstract

Maximum Marginal Relevance (MMR) Summarization of text is very important in grasping quickly long articles particularly for people who are very busy. In this paper, we use LDA to give topic queries for news articles, which then become inputs to the MMR method. According to this paper's summarization system, the ROUGE metric is employed to evaluate the summaries of news articles with 30 percent compression and 50 percent compression. Experimental findings show that the LDA-MMR combination outperforms MMR on its own in all our tests across all query lengths or number of sentences used and gives highest average ROUGE value of 0.570 for a 50% compression rate; 0.547 at 30% This implies that our system efficiently produces meaningful summaries using content-based keywords rather than click bait titles, which should not lead to complaints about misleading advertisements. This summarizer can convey the main points of a piece of news coverage in a concise form, thus offering people useful new tools for quickly digesting information.

Item Type:	Journal Article
Keywords:	Indonesian summarization; LDA; MMR; ROUGE evaluation
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080107 Natural Language Processing
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	Muhammad Faisal
Date Deposited:	17 Sep 2024 12:16

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