Less is More: Quality-Aware Training Data Selection for Scientific Summarization
arXiv·medium signal
This work proposes quality-aware training-data selection for scientific long-document summarization, arguing that author-written abstracts are noisy gold references and that filtering them improves results while using less data. Directly applicable to anyone curating summarization or instruction-tuning datasets where data quality outweighs raw volume.