Skripsi
ANALISIS KINERJA MODEL KEYPHRASE GENERATION BERBASIS PARADIGMA ONE2SET PADA ARTIKEL BERITA
The rapid growth of digital content makes online news management increasingly challenging, as vast document streams demand compact representations of core information. Keyphrases provide concise lexical surrogates for main topics and support indexing, retrieval, and summarization, yet limited annotations motivate automatic systems that can predict both present and absent keyphrases. This study develops a keyphrase generation system using a Transformer architecture, the SETTRANS model within the ONE2SET paradigm, which casts prediction as a set through control codes, K-step assignment, and bipartite matching to reduce order bias and duplication. The model is trained on 30,000 KPTimes articles. Experimental factors include assign_steps at 1, 2, and 3, learning rates at 0.0001 and 0.00003, and decoding strategies using greedy and beam search with beam size 2 and 5. Evaluation with the F1-Score shows the best configuration at assign_steps 3, learning rate 0.00003, and beam size 2, yielding F1@5 of 0.24, F1@10 of 0.20, and F1@M of 0.25. The results indicate that the larger K strengthens set alignment and broadens coverage without increasing duplication, the smaller learning rate stabilizes score calibration and improves overall consistency, and beam size 2 offers a balanced search that sustains accuracy while remaining time efficient and limiting redundant generations.
| Inventory Code | Barcode | Call Number | Location | Status |
|---|---|---|---|---|
| 2507006211 | T185547 | T1855472025 | Central Library (Reference) | Available but not for loan - Not for Loan |