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 selectio
The proliferation of digital information and the limitations of single-model summarization approaches make advanced frameworks like MASF increasingly relevant.
Improving abstractive text summarization directly enhances the efficiency of information processing, critical for decision-making in various sectors.
The ability to produce more robust and higher-quality summaries across diverse content will improve and become more reliable through adaptive multi-model approaches.
- · AI software developers
- · Information services
- · Content creators
- · Traditional manual summarization services
- · Single-model AI summarization platforms
Improved abstractive summarization tools become available to users.
Reduced human effort and time spent in understanding large volumes of text across industries.
Accelerated research and development cycles due to more efficient information assimilation and knowledge synthesis.
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