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IEEE/ICACT20260145 Question.1 Questioner: Lee19711936516@163.com 2026-02-09 오후 4:31:39	IEEE/ICACT20260145 Answer.1 Answer by Auhor m11407505@mail.ntust.edu.tw 2026-02-09 오후 4:31:39
What is the core reason why metaheuristic algorithms (such as NSGA-II) showed a particularly significant improvement in the ROUGE-2 metric compared to LM based baseline models in the extraction summarization task of this study? Why do these algorithms perform better in capturing two word combination content?	The superior performance of meta-heuristic algorithms on ROUGE-2 can be attributed to their global optimization nature. ROUGE-2 measures bigram overlap, which depends not only on the importance of individual sentences but also on whether related content units are jointly selected. LM-only baselines typically score sentences independently based on semantic similarity, without explicitly modeling inter-sentence interactions. In contrast, the proposed meta-heuristic framework optimizes sentence combinations under coverage and redundancy constraints, which naturally favors the preservation of adjacent content units. As a result, the algorithm is more effective at retaining meaningful bigram structures, leading to higher ROUGE-2 scores.

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