ReAct: A Review Comment Dataset for Actionability (and more)
This provides a resource for researchers working on document review and comment analysis, but it is incremental as it focuses on dataset creation rather than novel methods.
The authors tackled the problem of managing large volumes of review comments in documents by introducing ReAct, an annotated dataset for actionability and comment types, sourced from OpenReview and validated through crowd-sourced annotations, with benchmark results showing performance on classification tasks.
Review comments play an important role in the evolution of documents. For a large document, the number of review comments may become large, making it difficult for the authors to quickly grasp what the comments are about. It is important to identify the nature of the comments to identify which comments require some action on the part of document authors, along with identifying the types of these comments. In this paper, we introduce an annotated review comment dataset ReAct. The review comments are sourced from OpenReview site. We crowd-source annotations for these reviews for actionability and type of comments. We analyze the properties of the dataset and validate the quality of annotations. We release the dataset (https://github.com/gtmdotme/ReAct) to the research community as a major contribution. We also benchmark our data with standard baselines for classification tasks and analyze their performance.