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New framework uses multiple models for better text summarization

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a Multi-Model Adaptive Summarization Framework (MASF) to enhance abstractive text summarization. This framework integrates multiple fine-tuned transformer models, each generating a summary for a given article. An adaptive selection mechanism then chooses the best summary based on lexical similarity and semantic relevance metrics. MASF demonstrated superior performance, achieving the highest BERTScore of 88.63% and outperforming models like GPT3-D2, Falcon-7b, and Mpt-7b on the CNN/DailyMail dataset. AI

IMPACT This framework could improve the quality and consistency of automated text summarization across diverse content types.

RANK_REASON The cluster contains an academic paper detailing a new framework for text summarization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework uses multiple models for better text summarization

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The cluster contains an academic paper detailing a new framework for text summarization. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Ahmed Alansary, Ali Hamdi ·

    MASF: A Multi-Model Adaptive Selection Framework for Abstractive Text summarization

    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…

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