6 citations · 15 across the 5 of their papers we have counts for
Showing cs.CLShow all
3 papers · 1 filter
cs.CL2023★ 3 cited
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…
cs.CL2023★ 5 cited
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…
cs.CL2022
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…