Text summarization is a subtask of natural language processing referring to the automatic creation of a concise and fluent summary that captures the main ideas and topics from one or multiple documents. Earlier literature surveys focus on extractive approaches, which rank the top-n most important sentences in the input document and then combine them to form a summary. As argued in the literature, the summaries of these approaches do not have the same lexical flow or coherence as summaries that are manually produced by humans. Newer surveys elaborate abstractive approaches, which generate a summary with potentially new phrases and sentences compared to the input document. Generally speaking, contrary to the extractive approaches, the abstractive ones create summaries that are more similar to those produced by humans. However, these approaches still lack the contextual representation needed to form fluent summaries. Recent advancements in deep learning and pretrained language models led to the improvement of many natural language processing tasks, including abstractive summarization. Overall, these surveys do not present a comprehensive evaluation framework that assesses the aforementioned approaches. Taking the above into account, the contribution of this survey is fourfold: (i) we provide a comprehensive survey of the state-of-the-art approaches in text summarization; (ii) we conduct a comparative evaluation of these approaches, using well-known datasets from the related literature, as well as popular evaluation scores such as ROUGE-1, ROUGE-2, ROUGE-L, ROUGE-LSUM, BLEU-1, BLEU-2 and SACREBLEU; (iii) we report on insights gained on various aspects of the text summarization process, including existing approaches, datasets and evaluation methods, and we outline a set of open issues and future research directions; (iv) we upload the datasets and the code used in our experiments in a public repository, aiming to increase the reproducibility of this work and facilitate future research in the field.

Text classification (TC) is a subtask of natural language processing (NLP) that categorizes text pieces into predefined classes based on their textual content and thematic aspects. This process typically includes the training of a Machine Learning (ML) model on a labeled dataset, where each text example is associated with a specific class. Recent progress in Deep Learning (DL) enabled the development of deep neural transformer models, surpassing traditional ML ones. In any case, works of the topic classification literature prioritize high-resource languages, particularly English, while research efforts for low-resource ones, such as Greek, are limited. Taking the above into consideration, this paper presents: (i) the first Greek social media topic classification dataset; (ii) a comparative assessment of a series of traditional ML models trained on this dataset, utilizing an array of text vectorization methods including TF-IDF, classical word and transformer-based Greek embeddings; (iii) a fine-tuned GREEK-BERT-based TC model on the same dataset; (iv) key empirical findings demonstrating that transformer-based embeddings significantly increase the performance of traditional ML models, while our fine-tuned DL model outperforms previous ones. The dataset, the best-performing model, and the experimental code are made public, aiming to augment the reproducibility of this work and advance future research in the field.

The advent of Generative AI has certainly boosted the interest in developing innovative chatbot applications. Despite a vast amount of machine learning (ML) and natural language processing (NLP) research and English language resources that greatly improve chatbot technology, the corresponding research and resources for the Greek language are limited. The contribution of this paper is twofold: (i) it reports on the state-of-the-art research in Greek NLP, as far as language resources, embeddings-based techniques, deep learning models, and existing chatbot applications are concerned; (ii) it offers a set of insights on current NLP models and chatbot implementation methodologies, and outlines a set of pending issues and future research directions.

In the research area of Natural Language Processing (NLP), text summarization (TS) has been defined as the automatic composition of a cohesive and articulate summary, which encapsulates the main ideas and themes from a single or multiple documents. Recent developments from the areas of Deep Learning (DL) and Natural Language Understanding (NLU) have facilitated the development of abstractive TS transformer-based models, which demonstrate better performance than classical extractive ones. In any case, the vast majority of current NLP research concentrates on high-resource languages (e.g., English). Dealing with modern Greek, this paper introduces and elaborates a novel abstractive TS dataset comprising 93,433 Greek Wikipedia articles and their summaries assigned by human editors. The paper also proposes a series of DL abstractive TS models that were fine-tuned on this dataset for the task of Greek article summarization. A thorough experimentation was conducted for the comparative assessment of the proposed models against well-known extractive summarization ones, using the test subset of the dataset. The results reveal that the proposed abstractive models outperform the extractive ones across various TS evaluation metrics. To enhance the reproducibility of our work, we make publicly available the corresponding dataset, our best performing model and the experimentation code.

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.