Summary of Deep Transfer Learning Based Peer Review Aggregation and Meta-review Generation For Scientific Articles, by Md. Tarek Hasan et al.
Deep Transfer Learning Based Peer Review Aggregation and Meta-review Generation for Scientific Articles
by Md. Tarek Hasan, Mohammad Nazmush Shamael, H. M. Mutasim Billah, Arifa Akter, Md Al Emran Hossain, Sumayra Islam, Salekul Islam, Swakkhar Shatabda
First submitted to arxiv on: 5 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning-based system for peer review aggregation is proposed in this paper, addressing two challenges faced by meta-reviewers: acceptance decision-making and meta-review generation. The authors employ traditional machine learning algorithms, pre-trained word embedding techniques like BERT, and a transfer learning model based on T5 to automate the process. Experimental results show that BERT outperforms other word embedding techniques for acceptance decision prediction, while fine-tuned T5 excels in meta-review generation. The system takes peer reviews and relevant features as input to produce a meta-review and make an acceptance decision. The proposed approach outperforms existing models in both tasks, with statistically significant improvements confirmed through the Wilcoxon signed-rank test. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a machine learning-based system for streamlining peer review processes. It tackles two key challenges faced by reviewers: deciding which papers to accept and generating meaningful reviews. The authors use special algorithms and natural language processing techniques like BERT to make these decisions more efficiently. Their approach can help reviewers make better choices faster, which is important because there are so many papers being submitted nowadays. |
Keywords
» Artificial intelligence » Bert » Embedding » Machine learning » Natural language processing » T5 » Transfer learning
