2023/11/13 by Nikolaos Giarelis, Giarelis, Nikolaos, Charalampos Mastrokostas +3
Computer Science · #68T07 #68T50 #Advanced Text Analysis Techniques #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #I.2.7 #Natural Language Processing Techniques #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2311.07767
openalex publication_date 2023/11/13 · openalex created_date 2023/11/16 · openalex updated_date 2026/07/28
Text summarization (TS) is a natural language processing (NLP) subtask pertaining to the automatic formulation of a concise and coherent summary that covers the major concepts and topics from one or multiple documents. Recent advancements in deep learning have led to the development of abstractive summarization transformer-based models, which outperform classical approaches. In any case, research in this field focuses on high resource languages such as English, while the corresponding work for low resource languages is still underdeveloped. Taking the above into account, this paper proposes a series of novel TS models for Greek news articles. The proposed models were thoroughly evaluated on the same dataset against GreekBART, which is the state-of-the-art model in Greek abstractive news summarization. Our evaluation results reveal that most of the proposed models significantly outperform GreekBART on various evaluation metrics. We make our evaluation code public, aiming to increase the reproducibility of this work and facilitate future research in the field.