MASF: A Multi-Model Adaptive Selection Framework for Abstractive Text summarization
Quick Answer
This paper shows that The Multi-Model Adaptive Selection Framework (MASF) enhances abstractive text summarization by integrating multiple fine-tuned transformer models, achieving a BERTScore of 88.63%, outperforming LLMs like GPT3-D2 and Falcon-7b.
Quick Take
This framework addresses the inconsistency in summarization quality across diverse articles, ensuring robust and high-quality outputs.
Key Points
- MASF integrates multiple transformer-based models for improved summarization quality.
- Achieved a BERTScore of 88.63%, the highest among compared methods.
- Outperformed several , including GPT3-D2 and Falcon-7b.
- Utilizes an adaptive selection mechanism for final summary output.
- Evaluated on the CNN/DailyMail news summarization dataset.
Paper Resources
Source Excerpt
arXiv:2606. 05494v1 Announce Type: new Abstract: Automatic text summarization has become increasingly important due to the rapid growth of digital textual information. This paper presents a Multi-Model Adaptive Summarization Framework designed to improve the robustness and quality of abstractive text summarization. Relying on a single model often leads to inconsistent summarization quality across articles with varying structures and topics.
To address this limitation, the proposed framework integrates multiple fine-tuned transformer-based summarization models and introduces an adaptive selection mechanism. …
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