CITEWORTH: Cite-Worthiness Detection for Improved Scientific Document Understanding
This paper discusses a new method for identifying sentences in scientific papers that reference other works, which can help improve how we understand and process scientific documents.
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- 1 Scientific documents are complex and often lack enough labelled data for training models.
- 2 CITEWORTH is a new dataset created to help identify citing sentences in scientific texts.
- 3 Using citations as training signals can enhance the performance of NLP models.
- 4 The Longformer model outperforms previous models like SciBERT in detecting cite-worthiness.
Introduction
The introduction discusses the challenges of building effective NLP systems for scientific text due to its domain-specific nature and the scarcity of labelled data. It highlights the potential of using inherent structures in scientific documents, such as citations, to create large labelled datasets for tasks like cite-worthiness detection.
RQ1: CITEWORTH Dataset Construction
This section addresses the construction of the CITEWORTH dataset for cite-worthiness detection, detailing the use of the S2ORC dataset and the importance of low noise in the curation process. It defines key terms like ‘citation span’ and ‘citation marker’ and emphasizes the need to remove citation markers to ensure model accuracy.
Data Filtering
The data filtering process is outlined, explaining the criteria for selecting candidate papers from the S2ORC dataset. It describes the checks performed on sentences to ensure they meet the requirements for cite-worthiness, focusing on citation formats and sentence structure.
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This paper discusses a new method for identifying sentences in scientific papers that reference other works, which can help improve how we understand and process scientific documents.
Scientific documents are complex and often lack enough labelled data for training models. CITEWORTH is a new dataset created to help identify citing sentences in scientific texts. Using citations as training signals can enhance the performance of NLP models.
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