Papers by Goran Nenadic
Do You Hear The People Sing? Key Point Analysis via Iterative Clustering and Abstractive Summarisation (2023.acl-long)
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| Challenge: | Argument summarisation is a promising but currently under-explored field. |
| Approach: | They propose a framework to generate key points from short texts in a task known as Key Point Analysis. |
| Outcome: | The proposed framework improves state-of-the-art in argument summarisation with performance improvement of 14 percentage points compared to ROUGE and human evaluation scores. |
Generating Synthetic Free-text Medical Records with Low Re-identification Risk using Masked Language Modeling (2025.naacl-srw)
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| Challenge: | Existing methods to generate medical records using Causal Language Modelling are limited due to privacy concerns. |
| Approach: | They propose a method for generating medical records using Masked Language Modelling using Causal language models. |
| Outcome: | The proposed method produces high-quality synthetic data with a re-identification risk of only 3.5% and a patient recall of 96%. |
Evaluation and LLM-Guided Learning of ICD Coding Rationales (2026.eacl-long)
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| Challenge: | Existing studies on the explainability of ICD coding rely on attention-based rationales and qualitative assessments conducted by physicians. |
| Approach: | They propose to evaluate the explainability of rationales in ICD coding using a multi-granular rationale-annotated dataset. |
| Outcome: | The proposed model improves the explainability of rationales in ICD coding by using human-annotated rationale-announced rationale models. |
A Framework for Evaluation of Machine Reading Comprehension Gold Standards (2020.lrec-1)
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| Challenge: | Existing literature on machine reading comprehension (MRC) data is limited on the data design of gold standards. |
| Approach: | They propose a framework to investigate linguistic features, lexical cues and ambiguity in MRC gold standards. |
| Outcome: | The proposed framework investigates the present linguistic features, required reasoning and background knowledge and factual correctness on the one hand, and the presence of lexical cues as a lower bound for the requirement of understanding on the other. |
EDU-level Extractive Summarization with Varying Summary Lengths (2023.findings-eacl)
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| Challenge: | Existing studies on extractive summarization use finer-grained elementary discourse units . few studies exploited finer grained EDUs with little analysis and justification for the extractive unit selection . |
| Approach: | They propose an extractive model with Varying summary lengths that extracts fixed top-k salient sentences from the document as a summary. |
| Outcome: | The proposed model performs better on ROUGE scores than state-of-the-art models. |
Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation (2024.findings-acl)
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Hao Li, Yuping Wu, Viktor Schlegel, Riza Batista-Navarro, Tharindu Madusanka, Iqra Zahid, Jiayan Zeng, Xiaochi Wang, Xinran He, Yizhi Li, Goran Nenadic
| Challenge: | Recent advances in large language models (LLMs) have made it difficult to build an automated debate system that helps people to synthesise persuasive arguments. |
| Approach: | They propose to use an argument mining dataset to capture the end-to-end process of preparing an argumentative essay for a debate. |
| Outcome: | The proposed dataset shows that it performs better on individual tasks than on human-centred evaluations. |
MedTem2.0: Prompt-based Temporal Classification of Treatment Events from Discharge Summaries (2023.acl-srw)
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| Challenge: | Clinical texts contain important temporal information, such as medication start and end dates, appointment dates, and diagnosis dates. |
| Approach: | They propose to use prompt-based learning and fine-tuning to classify temporal relations between treatments and hospitalisation periods in discharge summaries. |
| Outcome: | The proposed method identifies whether a treatment was administered between the time of admission and discharge from the hospital. |
CAST: Corpus-Aware Self-similarity Enhanced Topic modelling (2025.naacl-long)
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Yanan Ma, Chenghao Xiao, Chenhan Yuan, Sabine N Van Der Veer, Lamiece Hassan, Chenghua Lin, Goran Nenadic
| Challenge: | Existing topic modelling methods encode contextual information of documents while ignoring contextual details of candidate centroid words. Existing methods are limited by the contextualization gap. |
| Approach: | They propose a topic modelling method that builds upon candidate centroid word embeddings contextualized on the dataset and a self-similarity-based method to filter out less meaningful tokens. |
| Outcome: | The proposed method significantly enhances the coherence and diversity of generated topics, and handles noisy data, outperforming strong baselines. |
INSIGHTBUDDY-AI: Medication Extraction and Entity Linking using Pre-Trained Language Models and Ensemble Learning (2025.naacl-srw)
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| Challenge: | InsightBuddy-AI is a system for extracting medication mentions and their associated attributes. |
| Approach: | They propose a system for extracting medication mentions and their associated attributes . they use stacked and voting ensembles built upon pre-trained language models . |
| Outcome: | The proposed system outperforms fine-tuned models in the extraction of medication mentions and associated attributes. |
Arg-LLaDA: Argument Summarization via Large Language Diffusion Models and Sufficiency-Aware Refinement (2026.acl-long)
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| Challenge: | Existing approaches to argument summarization rely on single-pass generation, offering limited support for factual correction or structural refinement. |
| Approach: | They propose a large language diffusion framework that iteratively improves argument summarization by sufficiency-guided remasking and regeneration. |
| Outcome: | Empirical results show that Arg-LLaDA surpasses state-of-the-art baselines in 7 out of 10 evaluation metrics. |