13 citations · 27 across the 6 of their papers we have counts for
6 papers
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…
MFair: Mitigating Bias in Healthcare Data through Multi-Level and Multi-Sensitive-Attribute Reweighting Method
Yinghao Zhu, Jingkun An, Enshen Zhou +8
In the data-driven artificial intelligence paradigm, models heavily rely on large amounts of training data. However, factors like sampling distribution imbalance can lead to issues…
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…
Do You Hear The People Sing? Key Point Analysis via Iterative Clustering and Abstractive Summarisation
Hao Li, Viktor Schlegel, Riza Batista-Navarro +1
Argument summarisation is a promising but currently under-explored field. Recent work has aimed to provide textual summaries in the form of concise and salient short texts, i.e., k…
Position-Aware Tagging for Aspect Sentiment Triplet Extraction
Lu Xu, Hao Li, Wei Lu +1
Aspect Sentiment Triplet Extraction (ASTE) is the task of extracting the triplets of target entities, their associated sentiment, and opinion spans explaining the reason for the se…
Learning Explicit and Implicit Structures for Targeted Sentiment Analysis
Hao Li, Wei Lu
Targeted sentiment analysis is the task of jointly predicting target entities and their associated sentiment information. Existing research efforts mostly regard this joint task as…