Kinerja BART dalam Automatic Summarization Berita Otomotif

CECILIANA TAKESHI; VINY CHRISTANTI MAWARDI; NOVARIO JAYA PERDANA

doi:10.26760/mindjournal.v10i1.61-72

Kinerja BART dalam Automatic Summarization Berita Otomotif

CECILIANA TAKESHI, VINY CHRISTANTI MAWARDI, NOVARIO JAYA PERDANA

Sari

ABSTRAK

Industri otomotif menghadapi tantangan besar dalam mengelola dan menyajikan informasi yang relevan serta terstruktur di tengah meningkatnya volume data digital. Penelitian ini memperkenalkan sistem pencarian dan peringkasan berbasis teks menggunakan model BART (Bidirectional and Auto-Regressive Transformers) untuk meningkatkan efisiensi pencarian informasi dan peringkasan konten. Sistem ini mengintegrasikan web scraping, preprocessing teks, TF-IDF, dan teknik cosine similarity untuk mengekstrak dan memproses data, menghasilkan hasil yang ringkas dan akurat. Evaluasi menggunakan metrik ROUGE dan umpan balik pengguna menunjukkan kemampuan sistem dalam menghasilkan ringkasan yang informatif dan efisien dengan waktu pemrosesan yang lebih cepat. Sistem ini mencapai performa sebesar rata rata 78.5 berdasarkan evaluasi yang dilakukan. Temuan ini menyoroti efektivitas BART dalam menangani data otomotif yang kompleks dan memenuhi kebutuhan pengguna untuk mendapatkan ringkasan berita yang relevan, mendukung pengambilan keputusan berbasis data di sektor otomotif.

Kata kunci: Pencarian Informasi, BART, NLP, Otomotif, Peringkasan Teks.

ABSTRACT

The automotive industry faces significant challenges in managing and presenting relevant and structured information amidst the growing volume of digital data. This study introduces a text-based search and summarization system using the BART (Bidirectional and Auto-Regressive Transformers) model to enhance the efficiency of information retrieval and content summarization. The system integrates web scraping, text preprocessing, TF-IDF, and cosine similarity techniques to extract and process data, delivering concise and accurate results. Evaluations using ROUGE metrics and user feedback demonstrate the system’s ability to produce informative and efficient summaries with reduced processing time. The system achieved a performance average score of 78.5 based on the evaluation. The findings highlight the effectiveness of BART in handling complex automotive data and meeting user needs relevant news summaries, thereby supporting data-driven decision-making in the automotive sector.

Keywords: Information Retrieval, BART, NLP, Automotive, Text Summarization.

Teks Lengkap:

PDF

Referensi

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Duchesnay, É. (2021). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825-2830.

Gao, Y., Xu, J., Hu, Z., & Han, W. (2021). Fine-Tuning Pre-trained Transformers for Abstractive Summarization of Long Documents in Low-Resource Languages. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1303-1315.

Zhu, X., & Lu, W. (2022). BART-based Summarization for Automotive News Articles: A Case Study. Journal of Natural Language Processing, 25(3), 211-223.

Goyal, N., Lewis, M., & Zettlemoyer, L. (2021). Efficient Fine-Tuning of Transformers for Text Classification in Low-Resource Languages. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), 710-720.

Nguyen, T., & Tran, T. (2022). Enhancing News Article Summarization with BART for Automotive Industry Applications. Proceedings of the 2022 International Conference on Artificial Intelligence, 153-160.

Zhang, Z., & Wang, X. (2021). Evaluating BART Model for Abstractive Text Summarization in Social Media. Proceedings of the 2021 IEEE 15th International Conference on Semantic Computing (ICSC), 145-152.

Zhang, Y., Sun, S., Galley, M., Chen, Y.-C., Brockett, C., Gao, X., ... & Dolan, B. (2021). DIALOGPT: Large-Scale Generative Pre-training for Conversational Response Generation. Proceedings of the 2021 Annual Meeting of the Association for Computational Linguistics, 2706-2715.

Zhou, L., & Lin, M. (2023). Web-Based BART Model for Summarizing Automotive News Articles Automatically. Journal of Web Engineering, 22(1), 45-58.

Narayan, S., Cohen, S. B., & Lapata, M. (2021). Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1797-1807.

Amplayo, R. K., & Lapata, M. (2021). Unsupervised Opinion Summarization with Content Planning. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), 6429-6440.

Chen, Y., & Zhao, Q. (2023). Automotive Text Summarization via BART and Fine-tuning for Web Applications. International Journal of Computational Linguistics, 34(2), 98-109.

Wang, S., & Li, F. (2022). Enhancing Automotive News Retrieval and Summarization with BART Model. Journal of Intelligent Systems, 37(1), 77-89.

Patel, A., & Singh, R. (2022). Web-based News Summarization for Automotive Domains Using BART. Proceedings of the 2022 International Conference on Natural Language Processing and Applications, 220-225.

Gao, P., & Wang, L. (2021). Automotive News Content Summarization via BART-based Approach. Journal of Web and Media Technology, 17(3), 145-156.

DOI: https://doi.org/10.26760/mindjournal.v10i1.61-72