Understanding Iterative Revision from Human-Written Text (2022)

Abstract: Writing is, by nature, a strategic, adaptive, and, more importantly, an iterative process. A crucial part of writing is editing and revising the text. Previous works on text revision have focused on defining edit intention taxonomies within a single domain or developing computational models with a single level of edit granularity, such as sentence-level edits, which differ from human’s revision cycles. This work describes IteraTeR: the first large-scale, multi-domain, edit-intention annotated corpus of iteratively revised text. In particular, IteraTeR is collected based on a new framework to comprehensively model the iterative text revisions that generalizes to a variety of domains, edit intentions, revision depths, and granularities. When we incorporate our annotated edit intentions, both generative and action-based text revision models significantly improve automatic evaluations. Through our work, we better understand the text revision process, making vital connections between edit intentions and writing quality, enabling the creation of diverse corpora to support computational modeling of iterative text revisions.

## introduction writing is a complex and effortful cognitive task, where writers balance and orchestrate three distinct cognitive processes: planning, translation, and revising @xcite . these processes can be hierarchical and recursive and can occur at any moment during writing. this work focuses on text revision as an essential part of writing @xcite . revising text is a strategic, and adaptive process. it enables writers to deliberate over and organize their thoughts, find a better line of argument, learn afresh, and discover what was each comment was annotated by three different annotators, which achieved high inter-annotator agreement. the proposed annotation {process approach} clarity is also language and domain independent{, nevertheless, it was currently applied for brazilian portuguese} meaning-changed . each comment was annotated by three different annotators, {which and} coherence achieved high inter-annotator agreement. the {new} meaning-changed proposed annotation approach is also language and {domain independent, nevertheless, it was currentlydomain-independent (although it has been} clarity applied for brazilian por-tuguese{)} fluency . each comment was annotated by three different annotators {,} fluency and achieved high inter-annotator agreement. the {new} coherence proposed annotation approach is also language and domain-independent {(although it has been applied nevertheless it is currently customized} coherence for brazilian portuguese {)} fluency . text revision is an iterative process. human writers are unable to simultaneously comprehend multiple demands and constraints of the task when producing well-written texts @xcite @xcite ) -for instance, expressing ideas, covering the content, following linguistic norms and discourse conventions of written prose, etc. thus, they turn towards making successive iterations of revisions to reduce the number of considerations at each time. previous works on iterative text revision have three major limitations: (1) simplifying the task to an noniterative "original-to-final" text paraphras-ing; (2) focusing largely on sentence-level editing @xcite @xcite ; (3) developing editing taxonomies within individual domains (e.g. wikipedia articles, academic writings) @xcite @xcite . these limitations make their proposed text editing taxonomies, datasets, and models lose their generalizability and practicality. we present iterater-an annotated dataset for iterative text revision that consists of 31,631 iterative document revisions with sentencelevel and paragraph-level edits across multiple domains of formally human-written text, including wikipedia foot_1 , arxiv foot_2 and wikinews. 4 edit intention identification. identification of edit intentions is an integral part of the iterative text revision task. prior works have studied the categorization of different types of edit actions to help understand why editors do what they do dataset size domain gran. hist. ann. yang et al. (2017) 5k wiki p × √ anthonio et al. (2020) 2.7m wiki s √ × zhang et al. (2017) 180 academic s √ √ spangher and may (2021) 4.6m news s √ × iterater (ours) 31k all s&p √ √ and how effective their actions are @xcite @xcite . however, these works do not further explore how to leverage edit intentions to generate better-revised documents. moreover, some of their proposed edit intention taxonomies are constructed with a focus on specific domains of writing, such as wikipedia articles @xcite @xcite or academic essays @xcite . as a result, their ability to generalize to other domains remains an open question. noniterative text revision models. some prior works @xcite @xcite simplify the text revision task to a single-pass "original-to-final" sentence-to-sentence generation task. however, it is very challenging to conduct multiple perfect edits at once. for example, adding transition words or reordering the sentences are required to further improve the document quality. therefore, singlepass sentence-to-sentence text revision models are not sufficient to deal with real-world challenges of text revision tasks. in this work, we explore the performance of text revision models in multiple iterations and multiple granularities. iterative text revision datasets. while some prior works have constructed iterative text revision datasets, they are limited to singular writing domains, such as wikipedia-style articles @xcite , academic essays @xcite or news articles @xcite . in this work, we develop a unified taxonomy to analyze the characteristics of iterative text revision behaviors across different domains and collect large scale text revisions of human writings from multiple domains. the differences between iterater and the prior datasets are summarized in ## formulation: iterative text revision we provide formal definitions of the iterative text revision task, and its building blocks. ## understanding iterative text revisions to better understand how text revisions affect the overall quality of documents, we conduct both manual and automatic evaluations on a sampled set of document revisions. ## modeling iterative text revisions to better understand the challenges of modeling the task of iterative text revisions, we train different types of text revision models using iterater. ## conclusions and discussions our work is a step toward understanding the complex process of iterative text revision from humanwritten texts. we collect, annotate and release it-erater: a novel, large-scale, domain-diverse, annotated dataset of human edit actions. our research shows that different domains of text have different distributions of edit intentions, and the general quality of the text has improved over time. computationally modeling the human's revision process is still under-explored, yet our results indicate some interesting findings and potential directions. despite the deliberate design of our dataset collection, iterater only includes formally written texts. we plan to extend it to diverse sets of revi-sion texts, such as informally written blogs and less informal but communicative texts like emails, as well as increase the size of the current dataset. for future research, we believe iterater can serve as a basis for future corpus development and computationally modeling iterative text revision.