Rambler：通过LLM辅助摘要操作支持语音写作

摘要

口述功能使移动设备上的文本输入更加高效。然而，语音书写可能会产生不流畅、冗长和不连贯的文本，因此需要进行大量的后期处理。本文介绍了Rambler，这是一个由LLM驱动的图形用户界面，支持对口述文本进行主旨级别的操作，具有两组主要功能：主旨提取和宏观修订。主旨提取生成关键词和摘要作为锚点，以支持审阅和与口述文本的交互。LLM辅助的宏观修订允许用户在不指定精确编辑位置的情况下重新讲述、拆分、合并和转换口述文本。它们共同为交互式口述和修订铺平道路，有助于弥合口语化的言辞和结构良好的书面表达之间的差距。在与12名参与者执行口头作文任务的比较研究中，Rambler胜过了基准组，即语音转文本编辑器+ChatGPT，因为它更好地促进了具有增强用户控制权的迭代修订，同时支持了令人惊讶地多样化的用户策略。

English

Dictation enables efficient text input on mobile devices. However, writing with speech can produce disfluent, wordy, and incoherent text and thus requires heavy post-processing. This paper presents Rambler, an LLM-powered graphical user interface that supports gist-level manipulation of dictated text with two main sets of functions: gist extraction and macro revision. Gist extraction generates keywords and summaries as anchors to support the review and interaction with spoken text. LLM-assisted macro revisions allow users to respeak, split, merge and transform dictated text without specifying precise editing locations. Together they pave the way for interactive dictation and revision that help close gaps between spontaneous spoken words and well-structured writing. In a comparative study with 12 participants performing verbal composition tasks, Rambler outperformed the baseline of a speech-to-text editor + ChatGPT, as it better facilitates iterative revisions with enhanced user control over the content while supporting surprisingly diverse user strategies.

Rambler：通过LLM辅助摘要操作支持语音写作

Rambler: Supporting Writing With Speech via LLM-Assisted Gist Manipulation

摘要

Support