Automatic Criminal News Summarization System with  Extractive Method Based on Latent

Christ Chandra

doi:10.59934/jaiea.v5i1.1395

Authors

Christ Chandra Mahasiswa

DOI:

https://doi.org/10.59934/jaiea.v5i1.1395

Keywords:

Summarization system, TF-IDF, LSA, ROUGE

Abstract

The rapid growth of digital information demands automatic systems to help users efficiently extract the core of information, especially in criminal news which often attracts significant public attention. This study aims to design and develop an automatic summarization system for criminal news using an extractive method based on Latent Semantic Analysis (LSA). In the process, textual features are first extracted using the Term Frequency-Inverse Document Frequency (TF-IDF) method to weigh the importance of each word in the document. The resulting TF-IDF matrix is then used as input for LSA to model semantic relationships between sentences and identify those most representative of the document content. The dataset consists of Indonesian-language criminal news articles collected from various online news portals. The system is evaluated by comparing the automatically generated summaries with human-written summaries using the ROUGE metric. The experimental results show that the combination of TF-IDF and LSA can generate informative and relevant summaries, achieving a ROUGE-1 score of 0.72. This system is expected to help users understand news content quickly and efficiently.

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