Exploitation and exploration in text evolution. Quantifying planning and translation flows during writing

• URL: http://arxiv.org/abs/2302.03645v2
• Date: Wed, 8 Feb 2023 11:25:23 GMT
• Title: Exploitation and exploration in text evolution. Quantifying planning and translation flows during writing
• Authors: Donald Ruggiero Lo Sardo, Pietro Gravino, Christine Cuskley and Vittorio Loreto
• Abstract summary: We introduce measures to quantify subcycles of planning (exploration) and translation (exploitation) during the writing process. This dataset comes from a series of writing workshops in which, through innovative versioning software, we were able to record all the steps in the construction of a text.
• Score: 0.13108652488669734
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Writing is a complex process at the center of much of modern human activity. Despite it appears to be a linear process, writing conceals many highly non-linear processes. Previous research has focused on three phases of writing: planning, translation and transcription, and revision. While research has shown these are non-linear, they are often treated linearly when measured. Here, we introduce measures to detect and quantify subcycles of planning (exploration) and translation (exploitation) during the writing process. We apply these to a novel dataset that recorded the creation of a text in all its phases, from early attempts to the finishing touches on a final version. This dataset comes from a series of writing workshops in which, through innovative versioning software, we were able to record all the steps in the construction of a text. More than 60 junior researchers in science wrote a scientific essay intended for a general readership. We recorded each essay as a writing cloud, defined as a complex topological structure capturing the history of the essay itself. Through this unique dataset of writing clouds, we expose a representation of the writing process that quantifies its complexity and the writer's efforts throughout the draft and through time. Interestingly, this representation highlights the phases of "translation flow", where authors improve existing ideas, and exploration, where creative deviations appear as the writer returns to the planning phase. These turning points between translation and exploration become rarer as the writing process progresses and the author approaches the final version. Our results and the new measures introduced have the potential to foster the discussion about the non-linear nature of writing and support the development of tools that can support more creative and impactful writing processes.

Related papers

• Agents' Room: Narrative Generation through Multi-step Collaboration [54.98886593802834]
  We propose a generation framework inspired by narrative theory that decomposes narrative writing into subtasks tackled by specialized agents. We show that Agents' Room generates stories preferred by expert evaluators over those produced by baseline systems.
  arXiv  Detail & Related papers  (2024-10-03T15:44:42Z)
• Teach LLMs to Personalize -- An Approach inspired by Writing Education [37.198598706659524]
  We propose a general approach for personalized text generation using large language models (LLMs) Inspired by the practice of writing education, we develop a multistage and multitask framework to teach LLMs for personalized generation.
  arXiv  Detail & Related papers  (2023-08-15T18:06:23Z)
• Decoding the End-to-end Writing Trajectory in Scholarly Manuscripts [7.294418916091011]
  We introduce a novel taxonomy that categorizes scholarly writing behaviors according to intention, writer actions, and the information types of the written data. Motivated by cognitive writing theory, our taxonomy for scientific papers includes three levels of categorization in order to trace the general writing flow. ManuScript intends to provide a complete picture of the scholarly writing process by capturing the linearity and non-linearity of writing trajectory.
  arXiv  Detail & Related papers  (2023-03-31T20:33:03Z)
• Sequentially Controlled Text Generation [97.22539956688443]
  GPT-2 generates sentences that are remarkably human-like, longer documents can ramble and do not follow human-like writing structure. We study the problem of imposing structure on long-range text. We develop a sequential controlled text generation pipeline with generation and editing.
  arXiv  Detail & Related papers  (2023-01-05T21:23:51Z)
• Summarization with Graphical Elements [55.5913491389047]
  We propose a new task: summarization with graphical elements. We collect a high quality human labeled dataset to support research into the task.
  arXiv  Detail & Related papers  (2022-04-15T17:16:41Z)
• Deep Learning for Text Style Transfer: A Survey [71.8870854396927]
  Text style transfer is an important task in natural language generation, which aims to control certain attributes in the generated text. We present a systematic survey of the research on neural text style transfer, spanning over 100 representative articles since the first neural text style transfer work in 2017. We discuss the task formulation, existing datasets and subtasks, evaluation, as well as the rich methodologies in the presence of parallel and non-parallel data.
  arXiv  Detail & Related papers  (2020-11-01T04:04:43Z)
• Positioning yourself in the maze of Neural Text Generation: A Task-Agnostic Survey [54.34370423151014]
  This paper surveys the components of modeling approaches relaying task impacts across various generation tasks such as storytelling, summarization, translation etc. We present an abstraction of the imperative techniques with respect to learning paradigms, pretraining, modeling approaches, decoding and the key challenges outstanding in the field in each of them.
  arXiv  Detail & Related papers  (2020-10-14T17:54:42Z)
• Characterizing Stage-Aware Writing Assistance in Collaborative Document Authoring [14.512030721220437]
  We present three studies that explore temporal stages of document authoring. We conclude that writers do in fact conceptually progress through several distinct phases while authoring documents. As a first step towards facilitating an intelligent digital writing assistant, we conduct a preliminary investigation into the utility of user interaction log data for predicting the temporal stage of a document.
  arXiv  Detail & Related papers  (2020-08-18T21:48:04Z)
• From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information [77.89755281215079]
  Text summarization is the research area aiming at creating a short and condensed version of the original document. In real-world applications, most of the data is not in a plain text format. This paper focuses on the survey of these new summarization tasks and approaches in the real-world application.
  arXiv  Detail & Related papers  (2020-05-10T14:59:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.