no code implementations • EMNLP (sdp) 2020 • Jiaxin Ju, Ming Liu, Longxiang Gao, Shirui Pan
The Scholarly Document Processing (SDP) workshop is to encourage more efforts on natural language understanding of scientific task.
1 code implementation • 12 Oct 2023 • Yizhen Zheng, Huan Yee Koh, Jiaxin Ju, Anh T. N. Nguyen, Lauren T. May, Geoffrey I. Webb, Shirui Pan
We present a method for using general-purpose large language models to make inferences from scientific datasets of the form usually associated with special-purpose machine learning algorithms.
1 code implementation • 4 Sep 2023 • Linhao Luo, Jiaxin Ju, Bo Xiong, Yuan-Fang Li, Gholamreza Haffari, Shirui Pan
Logical rules are essential for uncovering the logical connections between relations, which could improve reasoning performance and provide interpretable results on knowledge graphs (KGs).
1 code implementation • 30 Oct 2022 • Huan Yee Koh, Jiaxin Ju, He Zhang, Ming Liu, Shirui Pan
For long document abstractive models, we show that the constant strive for state-of-the-art ROUGE results can lead us to generate more relevant summaries but not factual ones.
1 code implementation • 3 Jul 2022 • Huan Yee Koh, Jiaxin Ju, Ming Liu, Shirui Pan
The empirical analysis includes a study on the intrinsic characteristics of benchmark datasets, a multi-dimensional analysis of summarization models, and a review of the summarization evaluation metrics.
no code implementations • Findings (EMNLP) 2021 • Jiaxin Ju, Ming Liu, Huan Yee Koh, Yuan Jin, Lan Du, Shirui Pan
This paper presents an unsupervised extractive approach to summarize scientific long documents based on the Information Bottleneck principle.
no code implementations • 19 Oct 2020 • Jiaxin Ju, Ming Liu, Longxiang Gao, Shirui Pan
The Scholarly Document Processing (SDP) workshop is to encourage more efforts on natural language understanding of scientific task.