Heuristic and LLM-Based RoI Extraction for Turkish News Pages Türkçe Haber Sayfalarinda Keşifsel ve LLM Tabanli ?Ilgi Bölgesi Çikarimi
34th Signal Processing and Communications Applications Conference, SIU 2026, İstanbul, Türkiye, 7 - 10 Temmuz 2026, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/siu71813.2026.11636889
- Basıldığı Şehir: İstanbul
- Basıldığı Ülke: Türkiye
- Anahtar Kelimeler: content extraction, HTML processing, information retrieval, large language models, news extraction, Turkish news pages
- Orta Doğu Teknik Üniversitesi Adresli: Evet
Özet
This study addresses the extraction of news content regions (Region of Interest, RoI) from Turkish news pages using both rule-based and large language model (LLM)-based methods. A heuristic container element is first selected from the HTML DOM; then either this container or the full page body is fed to the LLM under different input types. A 2 × 3 factorial design crossing two scope levels (container/body) with three input types (HTML / Flow-Filtered / Flow-AllText) is evaluated on 100 news samples using weighted token-multiset F1 and ROUGE-L F1. Results show that HTML-based inputs consistently outperform flow-based alternatives across all models, with body + HTML achieving the highest quality and container+HTML offering the best quality-cost trade-off.