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2026-10-22, Week 43
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Virual Oral Question and Answer
IEEE/ICACT20260145 Question.2
Questioner: Lee19711936516@163.com    2026-02-09 오후 4:32:07 		IEEE/ICACT20260145 Answer.2
Answer by Auhor m11407505@mail.ntust.edu.tw   2026-02-09 오후 4:32:07
What are the key factors that enable the two-stage pipeline of "meta heuristic candidate generation+LM assisted rearrangement" proposed in the study to maintain performance advantages in cross domain generalization (such as from news corpus CNN/DailyMail to technology abstract corpus SciTLDR-AIC)? How does this framework adapt to the task characteristics of different corpora? The proposed framework exhibits strong cross-domain generalization because its optimization objectives are largely domain-agnostic. Instead of learning dataset-specific patterns, the method formulates extractive summarization as a multi-objective search problem that optimizes coverage, redundancy, and length constraints—criteria that remain consistent across different summarization domains. Moreover, the two-stage design explicitly separates structural candidate generation from semantic refinement. In Stage 1, NSGA-II explores sentence combinations based on global summary-level properties, which are insensitive to domain-specific language distributions. In Stage 2, pre-trained language models are used only as auxiliary semantic scorers to re-rank candidates, rather than as primary decision-makers. This modular design allows the framework to adapt to different datasets without retraining or feature redesign, explaining why stable performance is maintained across news and scientific summarization tasks.

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