GreekT5: A Series of Greek Sequence-to-Sequence Models for News
  Summarization
Nikolaos Giarelis Charalampos Mastrokostas Nikos Karacapilidis
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.