Enhancing Indonesian Text Summarization with Latent Dirichlet Allocation and Maximum Marginal Relevance

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.

@article{Faisal2024,
  title     = {Enhancing Indonesian Text Summarization with Latent Dirichlet Allocation and Maximum Marginal Relevance},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {8},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Muhammad Faisal and Bima Hamdani Mawaridi and Ashri Shabrina Afrah and Supriyono and Yunifa Miftachul Arif and Abdul Aziz and Linda Wijayanti and Melisa Mulyadi},
  doi       = {10.14569/IJACSA.2024.0150852},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150852}
}