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Recommending Missed Citations Identified by Reviewers: A New Task, Dataset, and Baselines

Core Concepts
The author introduces a novel task of Recommending Missed Citations Identified by Reviewers (RMC) to enhance the credibility and validity of research. They propose a new framework, RMCNet, that outperforms previous methods in all metrics.
The content discusses the challenges of comprehensive citation recommendations due to the exponential growth of scientific publications. It introduces the RMC task to improve citations in submissions identified as lacking vital references. The authors curate a dataset called CitationR from real reviews and evaluate various state-of-the-art methods on it. They propose the RMCNet framework with an Attentive Reference Encoder module for mining citation relevance between papers. Key points: Current citation recommendation systems face challenges due to the vast amount of scientific publications. The RMC task aims to recommend missed citations identified by reviewers to enhance research credibility. The authors introduce the CitationR dataset extracted from real reviews for evaluation. Various state-of-the-art methods are evaluated on CitationR, with RMCNet outperforming them. The Attentive Reference Encoder module is crucial for mining citation relevance between papers.
"In total, we collect 14,520 unique papers recommended by reviewers." "Out of 21,598 collected submissions, 7,528 papers (around 35%) are identified as missing citations." "Out of 76,143 collected reviews, 9,100 (around 12%) reviews contain citations recommended by reviewers."
"Inspired by this common phenomenon in the peer review process, we formulate and study a novel task of Recommending Missed Citations Identified by Reviewers (RMC)." "Our proposed method achieves the best results in all metrics on CitationR." "We make all code and data publicly available to motivate future research."

Key Insights Distilled From

by Kehan Long,S... at 03-05-2024
Recommending Missed Citations Identified by Reviewers

Deeper Inquiries

How can machine learning models be used responsibly to avoid bias amplification when recommending citations

Machine learning models can be used responsibly to avoid bias amplification when recommending citations by implementing several strategies: Diverse Training Data: Ensuring that the training data used for the model is diverse and representative of different perspectives, disciplines, and authors can help mitigate bias amplification. Bias Detection Algorithms: Implementing algorithms that detect and flag potential biases in the recommendations made by the model can help researchers review and adjust these recommendations accordingly. Regular Auditing: Regularly auditing the model's outputs to identify any patterns of bias or unfairness in citation recommendations is essential. This process allows for continuous improvement and refinement of the model. Incorporating Ethical Guidelines: Adhering to ethical guidelines such as fairness, transparency, accountability, and privacy throughout the development and deployment of machine learning models can help prevent bias amplification. User Feedback Mechanisms: Providing users with mechanisms to provide feedback on recommended citations can help identify biased recommendations and improve the overall quality of suggestions.

What are some potential ethical considerations when using datasets like CitationR for academic research

When using datasets like CitationR for academic research, some potential ethical considerations include: Privacy Concerns: Ensuring that personal information in reviews or submissions is anonymized to protect the privacy of reviewers and authors. Data Security: Implementing robust security measures to safeguard sensitive information contained within reviews or submissions from unauthorized access or breaches. Transparency: Being transparent about how data was collected, processed, labeled, and utilized in research studies involving CitationR dataset. Informed Consent: Obtaining informed consent from reviewers/authors whose data is included in CitationR before using it for research purposes. Avoiding Harm: Taking precautions to ensure that no harm comes to individuals associated with the dataset due to its use in academic research.

How can LLMs be leveraged further to enhance understanding and identification of missed citations in academic manuscripts

To leverage Large Language Models (LLMs) further for enhancing understanding and identification of missed citations in academic manuscripts: LLMs could be trained on a larger corpus containing a more extensive collection of scientific papers across various disciplines to enhance their knowledge base regarding relevant literature. Fine-tuning LLMs specifically on tasks related to identifying missed citations could improve their performance by focusing their learning on this specific domain. 3.Implementing multi-task learning approaches where LLMs are trained not only on citation recommendation but also other related tasks like summarization or document classification could enhance their ability to understand context better. 4.Collaborating with domain experts such as researchers or librarians during training phases could provide valuable insights into what constitutes a significant citation within specific fields. 5.Including additional features such as metadata about papers (e.g., publication year, journal impact factor) alongside text content during training may offer richer contextual information for LLMs when making citation recommendations