Work

Dynamic optimization of min-df in the GreedSum algorithm for enhanced extractive summarization

Year: 2025

Type: article

Abstract: This study introduces a dynamic optimization technique for the min-df (minimum document frequency) parameter in the GreedSum algorithm, aimed at improving extractive summarization. We applied geometric, percentile-based, and clustering-based methods on a dataset of 17,038 scientific articles from arXiv and PubMed.The results indicate a 2% improvement in ROUGE-1 F-measure compared to fixed min-df settings, achieving a peak F-measure of 45%. These findings demonstrate the potential of dynamic min-df tuning for better summa-rization, with future research directed at integrating deep learning models to further enhance accuracy and efficiency.

Source: Artificial Intelligence Review

DOI: 10.1007/s10462-025-11276-w

Authors: Shakarim Aubakirov, Iskander Akhmetov, Alexander Gelbukh, Rustam Mussabayev

Institutions: Kazakh-British Technical University, Satbayev University, Instituto Politécnico Nacional

Language: English

Topic: Advanced Text Analysis Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,190

Funder: Ministry of Science and Higher Education of the Republic of Kazakhstan