3 articles tagged with #abstractive-summarization. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers propose a BART-based hierarchical approach for Vietnamese multi-document abstractive summarization, achieving a ROUGE2-F1 score of 0.2468 on the VLSP 2022 benchmark. The method uses a novel document-shortening strategy guided by golden summaries and includes additional training data for the Vietnamese NLP community.
Researchers propose MASF, a Multi-Model Adaptive Selection Framework that combines multiple fine-tuned transformer models with automatic evaluation metrics to improve abstractive text summarization quality. The framework achieves a BERTScore of 88.63% on the CNN/DailyMail dataset, outperforming several large language models including GPT3-D2 and Falcon-7b.
Researchers developed EWAD and CPDP techniques for improving multi-teacher knowledge distillation in low-resource abstractive summarization tasks. The study across Bangla and cross-lingual datasets shows logit-level knowledge distillation provides most reliable gains, while complex distillation improves short summaries but degrades longer outputs.
