An Empirical Analysis of Edit Importance between Document Versions
Tanya Goyal | Sachin Kelkar | Manas Agarwal | Jeenu Grover
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
In this paper, we present a novel approach to infer significance of various textual edits to documents. An author may make several edits to a document; each edit varies in its impact to the content of the document. While some edits are surface changes and introduce negligible change, other edits may change the content/tone of the document significantly. In this paper, we perform an analysis on the human perceptions of edit importance while reviewing documents from one version to the next. We identify linguistic features that influence edit importance and model it in a regression based setting. We show that the predicted importance by our approach is highly correlated with the human perceived importance, established by a Mechanical Turk study.