Weicong Tan


2023

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DeakinNLP at ProbSum 2023: Clinical Progress Note Summarization with Rules and Language ModelsClinical Progress Note Summarization with Rules and Languague Models
Ming Liu | Dan Zhang | Weicong Tan | He Zhang
The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks

This paper summarizes two approaches developed for BioNLP2023 workshop task 1A: clinical problem list summarization. We develop two types of methods with either rules or pre-trained language models. In the rule-based summarization model, we leverage UMLS (Unified Medical Language System) and a negation detector to extract text spans to represent the summary. We also fine tune three pre-trained language models (BART, T5 and GPT2) to generate the summaries. Experiment results show the rule based system returns extractive summaries but lower ROUGE-L score (0.043), while the fine tuned T5 returns a higher ROUGE-L score (0.208).

2021

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Transformer over Pre-trained Transformer for Neural Text Segmentation with Enhanced Topic Coherence
Kelvin Lo | Yuan Jin | Weicong Tan | Ming Liu | Lan Du | Wray Buntine
Findings of the Association for Computational Linguistics: EMNLP 2021

This paper proposes a transformer over transformer framework, called Transformerˆ2, to perform neural text segmentation. It consists of two components: bottom-level sentence encoders using pre-trained transformers, and an upper-level transformer-based segmentation model based on the sentence embeddings. The bottom-level component transfers the pre-trained knowledge learnt from large external corpora under both single and pair-wise supervised NLP tasks to model the sentence embeddings for the documents. Given the sentence embeddings, the upper-level transformer is trained to recover the segmentation boundaries as well as the topic labels of each sentence. Equipped with a multi-task loss and the pre-trained knowledge, Transformerˆ2 can better capture the semantic coherence within the same segments. Our experiments show that (1) Transformerˆ2$manages to surpass state-of-the-art text segmentation models in terms of a commonly-used semantic coherence measure; (2) in most cases, both single and pair-wise pre-trained knowledge contribute to the model performance; (3) bottom-level sentence encoders pre-trained on specific languages yield better performance than those pre-trained on specific domains.