Papers by Md Rakibul Hasan
CSTRL: Context-Driven Sequential Transfer Learning for Abstractive Radiology Report Summarization (2025.findings-acl)
Copied to clipboard
Mst. Fahmida Sultana Naznin, Adnan Ibney Faruq, Mostafa Rifat Tazwar, Md Jobayer, Md. Mehedi Hasan Shawon, Md Rakibul Hasan
| Challenge: | Pretrained models that excel in abstractive summarization problems face challenges when applied to specialized medical domains due to complex terminology and the necessity for accurate clinical context. |
| Approach: | They propose a sequential transfer learning model that ensures key content extraction and coherent summarization. |
| Outcome: | The proposed model shows 56.2% improvement in BLEU-1, 40.5% in ble-2, 84.3% in blu-3, 28.9% in ROUGE-1, 41.0% in Rough-2 and 26.5% of ROGUE-3 over benchmark studies. |
Thesis Proposal: Detecting Empathy Using Multimodal Language Model (2024.eacl-srw)
Copied to clipboard
| Challenge: | Existing studies on empathy detection in video and audio have relied on scripted or semi-scripted interactions that fail to capture the complexities and nuances of real-life interactions. |
| Approach: | They propose to develop a multimodal language model that detects empathy in audiovisual data by using neural architecture search and optimisation techniques. |
| Outcome: | The proposed model will be able to detect empathy in audiovisual data and use neural architecture search to deliver it. |
LLM-GEm: Large Language Model-Guided Prediction of People’s Empathy Levels towards Newspaper Article (2024.findings-eacl)
Copied to clipboard
| Challenge: | Empathy is a key component of human-to-human interactions, and is often overlooked due to the inherent noise in crowdsourced annotations. |
| Approach: | They propose a large language model-guided empathy prediction system that rectifies annotation errors based on defined annotation selection threshold and makes annotations reliable for conventional empathy prediction models. |
| Outcome: | The proposed system rectifies annotation errors based on defined selection threshold and makes the annotations reliable for conventional empathy prediction models, e.g., BERT-based pre-trained language models. |