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.
This video presentation explains the key concepts from the paper in plain language.
Content & Liability Disclaimer
This article and its accompanying video are automated summaries derived from the original research paper by Unknown authors. The original research was conducted solely by the paper's authors; PDFdigest did not conduct any of the research and makes no claims of ownership over the underlying scientific work.
The video narration is generated by artificial intelligence and references the paper's authors for attribution. The video is not narrated by any of the paper's authors. This content may contain inaccuracies, omissions, or misinterpretations of the original research. First-person language (e.g., "we found", "our results") reflects the original authors' voice, not PDFdigest's. Always read the original paper for accurate, verified information before making any decisions based on this content.
This content is provided "as is" without any warranties, express or implied. Simulated systems OÜ, its officers, directors, employees, and agents shall not be liable for any direct, indirect, incidental, special, consequential, or punitive damages arising from your use of, reliance on, or access to this content, including but not limited to errors, omissions, or misinterpretations of the original research. This disclaimer applies to the fullest extent permitted by applicable law.
- 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.
How PDFdigest Helps You Understand Research
Instant Paper Analysis
Get structured summaries and key findings from dense PDFs in seconds.
Visual Explanations
Turn complex methods, figures, and results into clearer visual breakdowns.
AI-Powered Q&A
Ask focused questions and get answers grounded in the paper.
Figures Explained
Frequently Asked Questions
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.
PDFdigest turns dense academic PDFs into structured summaries, visual explanations, narrated videos, and question-answer workflows so you can understand the research faster.