338 citations · 698 across the 54 of their papers we have counts for
59 papers
Overview of BioASQ 2026: The fourteenth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering
Anastasios Nentidis, Georgios Katsimpras, Anastasia Krithara +18
This paper presents an overview of the fourteenth edition of the BioASQ challenge, organized in the context of the Conference and Labs of the Evaluation Forum (CLEF) 2026. BioASQ i…
Loss-Based Active Learning for Neural Abstractive Summarization
Michail Ioannou, Tatiana Passali, George Michalopoulos +1
Fine-tuning abstractive summarization models requires high-quality annotated data. However, obtaining such corpora is expensive and time-consuming, as it requires human annotators…
Sub-Billion, Super-Frontier: Small Language Models Rival Zero-Shot Frontier LLMs on General and Literary Relation Extraction
Despina Christou, Grigorios Tsoumakas
Large language models (LLMs) achieve strong relation extraction (RE), but their computational demands and reliance on proprietary APIs limit deployment in resource-constrained or p…
Addressing Imbalance in Multi-Label Data via Label-Specific Distance-based Oversampling
Bin Liu, Jun Wu, Haoyu Peng +4
The complex imbalanced label distribution poses a crucial challenge to multi-label classification, as most classifiers are biased towards the majority class and high-frequent label…
D2ACE: Multi-Label Batch Selection Guided by Dual Dynamics and Adaptive Correlation Enhancement
Bin Liu, Haoyu Peng, Zhijia Wei +2
Batch selection is crucial for improving both training efficiency and predictive performance in deep multi-label classification (MLC). Existing batch selection methods typically re…
What's the Best Way to Retrieve Slides? A Comparative Study of Multimodal, Caption-Based, and Hybrid Retrieval Techniques
Petros Stylianos Giouroukis, Dimitris Dimitriadis, Dimitrios Papadopoulos +2
Slide decks, serving as digital reports that bridge the gap between presentation slides and written documents, are a prevalent medium for conveying information in both academic and…
Overview of BioASQ 2025: The Thirteenth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering
Anastasios Nentidis, Georgios Katsimpras, Anastasia Krithara +17
This is an overview of the thirteenth edition of the BioASQ challenge in the context of the Conference and Labs of the Evaluation Forum (CLEF) 2025. BioASQ is a series of internati…
DUAL: Diversity and Uncertainty Active Learning for Text Summarization
Petros Stylianos Giouroukis, Alexios Gidiotis, Grigorios Tsoumakas
With the rise of large language models, neural text summarization has advanced significantly in recent years. However, even state-of-the-art models continue to rely heavily on high…
Batch Selection for Multi-Label Classification Guided by Uncertainty and Dynamic Label Correlations
Ao Zhou, Bin Liu, Jin Wang +1
The accuracy of deep neural networks is significantly influenced by the effectiveness of mini-batch construction during training. In single-label scenarios, such as binary and mult…
Multi-Label Adaptive Batch Selection by Highlighting Hard and Imbalanced Samples
Ao Zhou, Bin Liu, Jin Wang +1
Deep neural network models have demonstrated their effectiveness in classifying multi-label data from various domains. Typically, they employ a training mode that combines mini-bat…
From Lengthy to Lucid: A Systematic Literature Review on NLP Techniques for Taming Long Sentences
Tatiana Passali, Efstathios Chatzikyriakidis, Stelios Andreadis +4
Long sentences have been a persistent issue in written communication for many years since they make it challenging for readers to grasp the main points or follow the initial intent…
Local Interpretability of Random Forests for Multi-Target Regression
Avraam Bardos, Nikolaos Mylonas, Ioannis Mollas +1
Multi-target regression is useful in a plethora of applications. Although random forest models perform well in these tasks, they are often difficult to interpret. Interpretability…
Does Noise Affect Housing Prices? A Case Study in the Urban Area of Thessaloniki
Georgios Kamtziridis, Dimitris Vrakas, Grigorios Tsoumakas
Real estate markets depend on various methods to predict housing prices, including models that have been trained on datasets of residential or commercial properties. Most studies e…
Large-scale investigation of weakly-supervised deep learning for the fine-grained semantic indexing of biomedical literature
Anastasios Nentidis, Thomas Chatzopoulos, Anastasia Krithara +2
Objective: Semantic indexing of biomedical literature is usually done at the level of MeSH descriptors with several related but distinct biomedical concepts often grouped together…
Truthful Meta-Explanations for Local Interpretability of Machine Learning Models
Ioannis Mollas, Nick Bassiliades, Grigorios Tsoumakas
Automated Machine Learning-based systems' integration into a wide range of tasks has expanded as a result of their performance and speed. Although there are numerous advantages to…
Fine-Grained Selective Similarity Integration for Drug-Target Interaction Prediction
Bin Liu, Jin Wang, Kaiwei Sun +1
The discovery of drug-target interactions (DTIs) is a pivotal process in pharmaceutical development. Computational approaches are a promising and efficient alternative to tedious a…
Artificial Disfluency Detection, Uh No, Disfluency Generation for the Masses
T. Passali, T. Mavropoulos, G. Tsoumakas +2
Existing approaches for disfluency detection typically require the existence of large annotated datasets. However, current datasets for this task are limited, suffer from class imb…
An Attention Matrix for Every Decision: Faithfulness-based Arbitration Among Multiple Attention-Based Interpretations of Transformers in Text Classification
Nikolaos Mylonas, Ioannis Mollas, Grigorios Tsoumakas
Transformers are widely used in natural language processing, where they consistently achieve state-of-the-art performance. This is mainly due to their attention-based architecture,…
Local Multi-Label Explanations for Random Forest
Nikolaos Mylonas, Ioannis Mollas, Nick Bassiliades +1
Multi-label classification is a challenging task, particularly in domains where the number of labels to be predicted is large. Deep neural networks are often effective at multi-lab…
Topic-Controllable Summarization: Topic-Aware Evaluation and Transformer Methods
Tatiana Passali, Grigorios Tsoumakas
Topic-controllable summarization is an emerging research area with a wide range of potential applications. However, existing approaches suffer from significant limitations. For exa…
Local Explanation of Dimensionality Reduction
Avraam Bardos, Ioannis Mollas, Nick Bassiliades +1
Dimensionality reduction (DR) is a popular method for preparing and analyzing high-dimensional data. Reduced data representations are less computationally intensive and easier to m…
Multiple Similarity Drug-Target Interaction Prediction with Random Walks and Matrix Factorization
Bin Liu, Dimitrios Papadopoulos, Fragkiskos D. Malliaros +2
The discovery of drug-target interactions (DTIs) is a very promising area of research with great potential. The accurate identification of reliable interactions among drugs and pro…
LARD: Large-scale Artificial Disfluency Generation
T. Passali, T. Mavropoulos, G. Tsoumakas +2
Disfluency detection is a critical task in real-time dialogue systems. However, despite its importance, it remains a relatively unexplored field, mainly due to the lack of appropri…
Bayesian Active Summarization
Alexios Gidiotis, Grigorios Tsoumakas
Bayesian Active Learning has had significant impact to various NLP problems, but nevertheless it's application to text summarization has been explored very little. We introduce Bay…