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20212026
most citedQuery-Focused Extractive Summarisation for Finding Ideal Answers to Biomedical and COVID-19 Questions

2 citations · 2 across the 7 of their papers we have counts for

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cs.CL2026

Cost-Pragmatic Quality Gating and Selection-Fusion Multi-Model Combiners for BioASQ Phases A+ and B

Dima Galat, Marian-Andrei Rizoiu

We describe our BioASQ Task 14B 2026 system. The work centers on two design decisions: how aggressively to re-retrieve when first-stage retrieval is weak, and how to combine multip…

cs.CL2025

LLM Ensemble for RAG: Role of Context Length in Zero-Shot Question Answering for BioASQ Challenge

Dima Galat, Diego Molla-Aliod

Biomedical question answering (QA) poses significant challenges due to the need for precise interpretation of specialized knowledge drawn from a vast, complex, and rapidly evolving…

cs.CL2024

Advancing LLM detection in the ALTA 2024 Shared Task: Techniques and Analysis

Dima Galat

The recent proliferation of AI-generated content has prompted significant interest in developing reliable detection methods. This study explores techniques for identifying AI-gener…

cs.CL2023

Enhancing Biomedical Text Summarization and Question-Answering: On the Utility of Domain-Specific Pre-Training

Dima Galat, Marian-Andrei Rizoiu

Biomedical summarization requires large datasets to train for text generation. We show that while transfer learning offers a viable option for addressing this challenge, an in-doma…

cs.CL20212 cited

Query-Focused Extractive Summarisation for Finding Ideal Answers to Biomedical and COVID-19 Questions

Diego Mollá, Urvashi Khanna, Dima Galat +2

This paper presents Macquarie University's participation to the BioASQ Synergy Task, and BioASQ9b Phase B. In each of these tasks, our participation focused on the use of query-foc…