CiteWorth: Cite-Worthiness Detection for Improved Scientific Document Understanding
This paper introduces a new dataset and model for identifying sentences in scientific papers that reference other works. It aims to improve how we understand and analyze scientific documents.
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- 1 CiteWorth is a new dataset that helps identify citations in scientific texts.
- 2 Using advanced models like Longformer can significantly improve citation detection.
- 3 Fine-tuning language models with citation tasks can enhance overall understanding of scientific documents.
Introduction
The introduction discusses the challenges of scientific document understanding and the need for large labeled datasets, highlighting the potential of citations as training signals.
Experiments and Results
Experiments demonstrate the performance of the CiteWorth model, showing a significant improvement in F1 score compared to existing models like SciBERT.
CiteWorth Dataset
This section details the creation of the CiteWorth dataset, emphasizing its size, quality, and the rigorous cleaning process applied to ensure its suitability for cite-worthiness detection.
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Model Development
The paper describes the development of a cite-worthiness detection model using Longformer, explaining its architecture and the advantages of paragraph-level contextualization over sentence-level models.
Conclusion
The conclusion summarizes the findings and implications of the study, particularly the benefits of using cite-worthiness detection in enhancing scientific document understanding.
Introduction
The introduction discusses the challenges of scientific document understanding and the need for large labeled datasets, highlighting the potential of citations as training signals.
CiteWorth Dataset
This section details the creation of the CiteWorth dataset, emphasizing its size, quality, and the rigorous cleaning process applied to ensure its suitability for cite-worthiness detection.
Model Development
The paper describes the development of a cite-worthiness detection model using Longformer, explaining its architecture and the advantages of paragraph-level contextualization over sentence-level models.
Experiments and Results
Experiments demonstrate the performance of the CiteWorth model, showing a significant improvement in F1 score compared to existing models like SciBERT.
Conclusion
The conclusion summarizes the findings and implications of the study, particularly the benefits of using cite-worthiness detection in enhancing scientific document understanding.
Frequently Asked Questions
This paper introduces a new dataset and model for identifying sentences in scientific papers that reference other works. It aims to improve how we understand and analyze scientific documents.
The introduction discusses the challenges of scientific document understanding and the need for large labeled datasets, highlighting the potential of citations as training signals.
Experiments demonstrate the performance of the CiteWorth model, showing a significant improvement in F1 score compared to existing models like SciBERT.
Yes. PDFDigest can turn this paper into a structured explanation, key takeaways, visual summaries, and a narrated video when available.