Handling Comments in Documents through Interactions (2022)

Abstract: Comments are widely used by users in collaborative documents every day. The documents’ comments enable collaborative editing and review dynamics, transforming each document into a context-sensitive communication channel. Understanding the role of comments in communication dynamics within documents is the first step towards automating their management. In this paper we propose the first ever taxonomy for different types of in-document comments based on analysis of a large scale dataset of public documents from the web. We envision that the next generation of intelligent collaborative document experiences allow interactive creation and consumption of content, there We also introduce the components necessary for developing novel tools that automate the handling of comments through natural language interaction with the documents. We identify the commands that users would use to respond to various types of comments. We train machine learning algorithms to recognize the different types of comments and assess their feasibility. We conclude by discussing some of the implications for the design of automatic document management tools.

## introduction comments on collaborative documents serve as a communication channel. this type of contextspecific communication allows dynamics to review and edit content within the document. collaborative text editors have visual components that allow users to associate a comment with a specific part of the content. this provides additional context in situations where the conversation focuses on a specific part of the document @xcite . as we can see, the amount of contextualization in communication that document comments permit is too complex and costly to recreate in other communications means outside of a document. for example, a request for changing a certain part of a document's content (e.g. a paragraph's sentence) through email would require much in the case of collaborative documents, there 124 are multiple elements that contribute to the con-125 text, such as the selected text, the paragraph 126 text, and the comment text. in this work, we 127 study intent detection models that use multiple 128 elements of the context. ## 177 this section explains the details of our process for preparing the document comment dataset. we have curated a set of documents that contain multiple comments from public sources available on the web. to our knowledge, there are currently no datasets available that have been curated for investigation of in-document comments. it is evident that such dataset is needed for research. although it is possible to investigate comments on public pages such as wikipedia, reddit, twitter, youtube, or other web forums, however, their use case of comments on these forums is inherently very different than in-document comments used for collaborative authoring. in-document comments are interactive and conversational and commonly request and result in changes and updates to the content of the document that is shared. in-document comments are intended to be carefully reviewed by the intended recipients, and authors and reviewers tend to resolve and remove them prior to releasing documents to the public readers. this practice makes it very difficult to come across in-document comments in public mature documents. private files which are earlier in the editing life-cycle are more likely to have threads of comments. we use public documents because releasing private files is not possible due to copyright and privacy concerns. in addition to the challenges mentioned, we observed that only a certain percentage of word documents from recent years (after 2003) support comments and that we were able to extract comments from them.