Sebastian Gehrmann, Lauren Urke, Ofra Amir, B. Grosz
{"title":"Deploying AI Methods to Support Collaborative Writing: a Preliminary Investigation","authors":"Sebastian Gehrmann, Lauren Urke, Ofra Amir, B. Grosz","doi":"10.1145/2702613.2732705","DOIUrl":null,"url":null,"abstract":"Many documents (e.g., academic papers, government reports) are typically written by multiple authors. While existing tools facilitate and support such collaborative efforts (e.g., Dropbox, Google Docs), these tools lack intelligent information sharing mechanisms. Capabilities such as \"track changes\" and \"diff\" visualize changes to authors, but do not distinguish between minor and major edits and do not consider the possible effects of edits on other parts of the document. Drawing collaborators' attention to specific edits and describing them remains the responsibility of authors. This paper presents our initial work toward the development of a collaborative system that supports multi-author writing. We describe methods for tracking paragraphs, identifying significant edits, and predicting parts of the paper that are likely to require changes as a result of previous edits. Preliminary evaluation of these methods shows promising results.","PeriodicalId":142786,"journal":{"name":"Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems","volume":"64 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2702613.2732705","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
Many documents (e.g., academic papers, government reports) are typically written by multiple authors. While existing tools facilitate and support such collaborative efforts (e.g., Dropbox, Google Docs), these tools lack intelligent information sharing mechanisms. Capabilities such as "track changes" and "diff" visualize changes to authors, but do not distinguish between minor and major edits and do not consider the possible effects of edits on other parts of the document. Drawing collaborators' attention to specific edits and describing them remains the responsibility of authors. This paper presents our initial work toward the development of a collaborative system that supports multi-author writing. We describe methods for tracking paragraphs, identifying significant edits, and predicting parts of the paper that are likely to require changes as a result of previous edits. Preliminary evaluation of these methods shows promising results.
许多文件(如学术论文、政府报告)通常由多位作者撰写。虽然现有的工具促进和支持这种协作工作(例如,Dropbox, Google Docs),但这些工具缺乏智能信息共享机制。诸如“跟踪更改”和“diff”之类的功能将更改可视化到作者,但不区分次要和主要编辑,也不考虑编辑对文档其他部分可能产生的影响。吸引合作者注意特定的编辑并对其进行描述仍然是作者的责任。本文介绍了我们为支持多作者写作的协作系统的开发所做的初步工作。我们描述了跟踪段落的方法,识别重要的编辑,并预测论文中可能由于先前的编辑而需要更改的部分。对这些方法的初步评价显示出令人满意的结果。