Deep Transfer Learning Based Peer Review Aggregation and Meta-review Generation for Scientific Articles (2410.04202v1)

Abstract: Peer review is the quality assessment of a manuscript by one or more peer experts. Papers are submitted by the authors to scientific venues, and these papers must be reviewed by peers or other authors. The meta-reviewers then gather the peer reviews, assess them, and create a meta-review and decision for each manuscript. As the number of papers submitted to these venues has grown in recent years, it becomes increasingly challenging for meta-reviewers to collect these peer evaluations on time while still maintaining the quality that is the primary goal of meta-review creation. In this paper, we address two peer review aggregation challenges a meta-reviewer faces: paper acceptance decision-making and meta-review generation. Firstly, we propose to automate the process of acceptance decision prediction by applying traditional machine learning algorithms. We use pre-trained word embedding techniques BERT to process the reviews written in natural language text. For the meta-review generation, we propose a transfer learning model based on the T5 model. Experimental results show that BERT is more effective than the other word embedding techniques, and the recommendation score is an important feature for the acceptance decision prediction. In addition, we figure out that fine-tuned T5 outperforms other inference models. Our proposed system takes peer reviews and other relevant features as input to produce a meta-review and make a judgment on whether or not the paper should be accepted. In addition, experimental results show that the acceptance decision prediction system of our task outperforms the existing models, and the meta-review generation task shows significantly improved scores compared to the existing models. For the statistical test, we utilize the Wilcoxon signed-rank test to assess whether there is a statistically significant improvement between paired observations.

Deep Transfer Learning Based Peer Review Aggregation and Meta-review Generation for Scientific Articles

The paper "Deep Transfer Learning Based Peer Review Aggregation and Meta-review Generation for Scientific Articles" addresses the intricate challenges faced by meta-reviewers amid the surging volume of scholarly submissions in scientific venues. This work seeks to present a dual-faceted approach: automating the paper acceptance decision-making process and generating meta-reviews to assist meta-reviewers in maintaining review quality and efficiency.

Overview and Methodologies

The research presents two primary challenges in peer review aggregation: the prediction of paper acceptance and the generation of meta-reviews. To tackle these issues, the authors propose employing traditional ML models for decision-making and a transfer learning approach for meta-review generation using a fine-tuned T5 model. They leveraged the BERT model for natural language processing to encode reviews which is central to both tasks. BERT's effectiveness as a word embedding technique over competitors, such as Word2Vec or fastText, is corroborated by the experimental observation that the recommendation score significantly influences acceptance prediction.

For meta-review generation, the authors utilized the Text-To-Text Transfer Transformer (T5), fine-tuning it on a newly created dataset from OpenReview and several international conferences. The paper's results highlight the T5 model's superior performance against prevailing inference models in generating coherent and contextually meaningful meta-reviews, with noteworthy improvements in metrics like ROUGE scores.

Experimental Insights

The paper presented a comprehensive set of experiments to underpin its findings. The ablation studies revealed critical insights into feature importance for acceptance decision prediction. Central elements such as the recommendation score and BERT's encoding were pivotal factors contributing to the model's accuracy and F1 scores, managing to outperform existing models significantly.

Regarding meta-review generation, employing the T5 model and experimenting with truncation strategies to counter computational constraints were key. TextRank and TextRank with sentiment analysis served as effective strategies for managing input size without significant loss of informational quality, leading to balanced and nuanced reviews.

Discussion of Results

The evaluations demonstrated robust performance, with statistical tests confirming the validity of their methodology. Specifically, the Decision Tree model, when combined with BERT embeddings, achieved the best results on accuracy and F1 scores for acceptance decisions, indicating its superior adaptability and robustness across datasets. In contrast, fine-tuned T5 outperformed other models like BERT and GPT in generating structured and readable meta-reviews.

Yet, the paper acknowledges the role of sentiment polarity in reviews and suggests incorporating sentiment analysis more deeply could further enhance model outcomes. This is particularly pertinent given the subjective nature of peer reviews and their influence on scholarly work evaluations.

Theoretical and Practical Implications

This research contributes vital insights into the efficacy of applying ML and NLP techniques to streamline academic workflows amidst an increasing volume of submissions. The implications are twofold: theoretically, it advances the understanding of transfer learning's role in textual summarization tasks; practically, it offers a scalable toolkit for academic committees to enhance their review processes.

Future Directions

The authors propose future explorations of deep learning architectures to refine acceptance decision-making further and suggest leveraging other transformers for meta-review generation. Moreover, improving truncation mechanisms and the contextual understanding of reviewer sentiment could yield further advancements in meta-review accuracy and reliability. This work lays a robust foundation for the continued evolution of automated academic review systems leveraging the latest advancements in AI and NLP.