Intelligent News Aggregation System with Automatic Classification, Clustering, and Summarization
DOI:
https://doi.org/10.47709/brilliance.v5i2.6712Keywords:
Artificial Intelligence, BART, BLSTM-2DCNN, Clustering, K-means, News Aggregation, Summarization, Text ClassificationAbstract
The rapid growth of online news content has made it increasingly difficult for users to access relevant information efficiently. This study presents the development of an intelligent web-based news aggregation system that performs automatic classification, clustering, and summarization of Indonesian-language news articles. The system aims to enhance the news reading experience by organizing articles by category and topic, and by providing concise summaries. The system was built using the ADDIE development model, with each AI component trained and evaluated separately. News classification is handled by a BLSTM-2DCNN model trained on the IndoSum dataset, achieving 86% accuracy and an F1-score of 0.85. This model was also applied to classify 37,187 real-world articles scraped from Kompas and TribunNews during June 2025. Topic clustering is performed using K-means with entropy-weighted Bag-of-Words features over 5-day sliding windows. The clustering quality, evaluated using the Calinski-Harabasz Index, ranged from 5.21 to 525.44 with an average of 80.53, indicating varying cluster cohesion. For summarization, a fine-tuned BART model was used to summarize the article closest to each cluster’s centroid. The model achieved ROUGE scores of 0.6389 (ROUGE-1), 0.5458 (ROUGE-2), and 0.6017 (ROUGE-L). The integrated system automatically scrapes news, classifies and clusters articles, and displays generated summaries through a user-friendly web interface. The results show that combining deep learning and natural language processing offers an effective approach for intelligent news aggregation, helping users consume news faster and more meaningfully.
References
Athey, S., Mobius, M., & Pal, J. (2021). The impact of aggregators on internet news consumption (No. w28746). National Bureau of Economic Research.
Chen, Tong, Xuewei Wang, Tianwei Yue, Xiaoyu Bai, Cindy X. Le, and Wenping Wang. 2023. “Enhancing Abstractive Summarization with Extracted Knowledge Graphs and Multi-Source Transformers.” Applied Sciences (Switzerland) 13(13). doi: 10.3390/app13137753.
Dewan Pers. 2024. “SURVEI LANSKAP MEDIA PERS INDONESIA.” Retrieved (https://dewanpers.or.id/berita/detail/2554/survei-lanskap-media-pers-indonesia).
Effendie, A. R. (2023). A medoid-based deviation ratio index to determine the number of clusters in a dataset. MethodsX, 10, 102084.
El-Kassas, Wafaa S., Cherif R. Salama, Ahmed A. Rafea, and Hoda K. Mohamed. 2021. “Automatic Text Summarization: A Comprehensive Survey.” Expert Systems with Applications 165:1–46. doi: 10.1016/j.eswa.2020.113679.
Hoe, C. W., Raheem, M., & Abubacker, N. F. (2024). News Aggregation And Summarisation. Journal of Applied Technology and Innovation (e-ISSN: 2600-7304), 8(4), 51.
Khder, M. A. (2021). Web scraping or web crawling: State of art, techniques, approaches and application. International Journal of Advances in Soft Computing & Its Applications, 13(3).
Kurniawan, Kemal, and Samuel Louvan. 2018. “IndoSum: A New Benchmark Dataset for Indonesian Text Summarization.” Proceedings of the 2018 International Conference on Asian Language Processing, IALP 2018 215–20. doi: 10.1109/IALP.2018.8629109.
Lin, Chin-Yew. 2004. “ROUGE: A Package for Automatic Evaluation of Summaries.” Japanese Circulation Journal. doi: 10.1253/jcj.34.1213.
Liu, Yixin, Kejian Shi, Katherine S. He, Longtian Ye, Alexander R. Fabbri, Pengfei Liu, Dragomir Radev, and Arman Cohan. 2023. “On Learning to Summarize with Large Language Models as References.”
Peng, Zhou, Zhenyu Qi, Suncong Zheng, Jiaming Xu, Hongyun Bao, and Xu Bo. 2016. “Text Classi?cation Improved by Integrating Bidirectional LSTM with Two-Dimensional Max Pooling.Pdf.” 3485–3495.
Rashid, Junaid, Syed Muhammad Adnan Shah, and Aun Irtaza. 2020. “An Efficient Topic Modeling Approach for Text Mining and Information Retrieval through K-Means Clustering.” Mehran University Research Journal of Engineering and Technology 39(1):213–22. doi: 10.22581/muet1982.2001.20.
Saravanos, A., & Curinga, M. X. (2023). Simulating the software development lifecycle: The waterfall model. Applied System Innovation, 6(6), 108.
Tian, Qiuxia. 2022. “Impact of Social Media News Overload on Social Media News Avoidance and Filtering: Moderating Effect of Media Literacy.” Frontiers in Psychology 13(March). doi: 10.3389/fpsyg.2022.862626.
Yeasmin, Sharmin, Ratnadip Kuri, A. R. M. Mahamudul Hasan Rana, Ashraf Uddin, A. Q. M. Sala Uddin Pathan, and Hasnat Riaz. 2021. “Multi-Category Bangla News Classification Using Machine Learning Classifiers and Multi-Layer Dense Neural Network.” International Journal of Advanced Computer Science and Applications 12(5):757–67. doi: 10.14569/IJACSA.2021.0120588.
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