DeepMetaGen: an unsupervised deep neural approach to generate template-based meta-reviews leveraging on aspect category and sentiment analysis from peer reviews

Peer reviews form an essential part of scientific communication. Scholarly peer review is probably the most accepted way to evaluate research papers by involving multiple experts to review the concerned research independently. Usually, the area chair, the program chair, or the editor takes a call weighing the reviewer's judgments. It communicates the decision to the author via writing a meta-review by summarizing the review comments. With the exponential rise in research paper submissions and the corresponding rise in the reviewer pool, it becomes stressful for the chairs/editors to manage conflicts, arrive at a consensus, and also write an informative meta-review. Here in this work, we propose a novel deep neural network-based approach for generating meta-reviews in an unsupervised fashion. To generate consistent meta-reviews, we use a generic template where the task is like to slot-fill the template with the generated meta-review text. We consider the setting where only peer reviews with no summaries or meta-reviews are provided and propose an end-to-end neural network model to perform unsupervised opinion-based abstractive summarization. We first use an aspect-based sentiment analysis model, which classifies the review sentences with the corresponding aspects (e.g., novelty, substance, soundness, etc.) and sentiment. We then extract opinion phrases from reviews for the corresponding aspect and sentiment labels. Next, we train a transformer model to reconstruct the original reviews from these extraction. Finally, we filter the selected opinions according to their aspect and/or sentiment at the time of summarization. The selected opinions of each aspect are used as input to the trained Transformer model, which uses them to construct an opinion summary. The idea is to give a concise meta-review that maximizes information coverage by focusing on aspects and sentiment present in the review, coherence, readability, and redundancy. We evaluate our model on the human written template-based meta-reviews to show that our framework outperforms competitive baselines. We believe that the template-based meta-review generation focusing on aspect and sentiment will help the editor/chair in decision-making and assist the meta-reviewer in writing better and more informative meta-reviews. We make our codes available athttps:// github.com/sandeep82945/Unsupervised-meta-review-generation.

Automated summary

Peer reviews are crucial in scientific communication, providing evaluation of research papers by multiple experts. This study proposes an unsupervised deep neural network approach for generating meta-reviews. Using a generic template, the method fills in the template with generated meta-review text to ensure consistency. This approach is beneficial for decision-making and helps in writing more informative meta-reviews.