Yanxia Qin


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The ACL OCL Corpus: Advancing Open Science in Computational Linguistics
Shaurya Rohatgi | Yanxia Qin | Benjamin Aw | Niranjana Unnithan | Min-Yen Kan
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

We present ACL OCL, a scholarly corpus derived from the ACL Anthology to assist Open scientific research in the Computational Linguistics domain. Integrating and enhancing the previous versions of the ACL Anthology, the ACL OCL contributes metadata, PDF files, citation graphs and additional structured full texts with sections, figures, and links to a large knowledge resource (Semantic Scholar). The ACL OCL spans seven decades, containing 73K papers, alongside 210K figures. We spotlight how ACL OCL applies to observe trends in computational linguistics. By detecting paper topics with a supervised neural model, we note that interest in “Syntax: Tagging, Chunking and Parsing” is waning and “Natural Language Generation” is resurging. Our dataset is available from HuggingFace (https://huggingface.co/datasets/WINGNUS/ACL-OCL).

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CocoSciSum: A Scientific Summarization Toolkit with Compositional Controllability
Yixi Ding | Yanxia Qin | Qian Liu | Min-Yen Kan
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We present a novel toolkit for controlled summarization of scientific documents, designed for the specific needs of the scientific community. Our system generates summaries based on user preferences, adjusting key attributes specifically of length and keyword inclusion. A distinguishing feature is its ability to manage multiple attributes concurrently, demonstrating Compositional Controllability for Scientific Summarization (CocoSciSum). Benchmarked against the strong Flan-T5 baseline, CocoSciSum exhibits superior performance on both the quality of summaries generated and the control over single and multiple attributes. Moreover, CocoSciSum is a user-centric toolkit, supporting user preferences expressed in natural language instructions, and accommodating diverse input document formats. CocoSciSum is available on GitHub (https://github.com/WING-NUS/SciAssist/tree/CocoSciSum) with an introduction video (https://youtu.be/YC1YDeEjAbQ).


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Lightweight Contextual Logical Structure Recovery
Po-Wei Huang | Abhinav Ramesh Kashyap | Yanxia Qin | Yajing Yang | Min-Yen Kan
Proceedings of the Third Workshop on Scholarly Document Processing

Logical structure recovery in scientific articles associates text with a semantic section of the article. Although previous work has disregarded the surrounding context of a line, we model this important information by employing line-level attention on top of a transformer-based scientific document processing pipeline. With the addition of loss function engineering and data augmentation techniques with semi-supervised learning, our method improves classification performance by 10% compared to a recent state-of-the-art model. Our parsimonious, text-only method achieves a performance comparable to that of other works that use rich document features such as font and spatial position, using less data without sacrificing performance, resulting in a lightweight training pipeline.


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Feature-Rich Segment-Based News Event Detection on Twitter
Yanxia Qin | Yue Zhang | Min Zhang | Dequan Zheng
Proceedings of the Sixth International Joint Conference on Natural Language Processing