5 citations · 17 across the 6 of their papers we have counts for
6 papers
Extract-and-Abstract: Unifying Extractive and Abstractive Summarization within Single Encoder-Decoder Framework
Yuping Wu, Hao Li, Goran Nenadic +1
Extract-then-Abstract is a naturally coherent paradigm to conduct abstractive summarization with the help of salient information identified by the extractive model. Previous works…
PULSAR at MEDIQA-Sum 2023: Large Language Models Augmented by Synthetic Dialogue Convert Patient Dialogues to Medical Records
Viktor Schlegel, Hao Li, Yuping Wu +8
This paper describes PULSAR, our system submission at the ImageClef 2023 MediQA-Sum task on summarising patient-doctor dialogues into clinical records. The proposed framework relie…
PULSAR: Pre-training with Extracted Healthcare Terms for Summarising Patients' Problems and Data Augmentation with Black-box Large Language Models
Hao Li, Yuping Wu, Viktor Schlegel +7
Medical progress notes play a crucial role in documenting a patient's hospital journey, including his or her condition, treatment plan, and any updates for healthcare providers. Au…
Exploring the Value of Pre-trained Language Models for Clinical Named Entity Recognition
Samuel Belkadi, Lifeng Han, Yuping Wu +1
The practice of fine-tuning Pre-trained Language Models (PLMs) from general or domain-specific data to a specific task with limited resources, has gained popularity within the fiel…
EDU-level Extractive Summarization with Varying Summary Lengths
Yuping Wu, Ching-Hsun Tseng, Jiayu Shang +3
Extractive models usually formulate text summarization as extracting fixed top- salient sentences from the document as a summary. Few works exploited extracting finer-grained El…
UPANets: Learning from the Universal Pixel Attention Networks
Ching-Hsun Tseng, Shin-Jye Lee, Jia-Nan Feng +5
Among image classification, skip and densely-connection-based networks have dominated most leaderboards. Recently, from the successful development of multi-head attention in natura…