Beyond Text Generation: Supporting Writers with Continuous Automatic Text Summaries.

要旨

We propose a text editor to help users plan, structure and reflect on their writing process. It provides continuously updated paragraph-wise summaries as margin annotations, using automatic text summarization. Summary levels range from full text, to selected (central) sentences, down to a collection of keywords. To understand how users interact with this system during writing, we conducted two user studies (N=4 and N=8) in which people wrote analytic essays about a given topic and article. As a key finding, the summaries gave users an external perspective on their writing and helped them to revise the content and scope of their drafted paragraphs. People further used the tool to quickly gain an overview of the text and developed strategies to integrate insights from the automated summaries. More broadly, this work explores and highlights the value of designing AI tools for writers, with Natural Language Processing (NLP) capabilities that go beyond direct text generation and correction.

著者
Hai Dang
University of Bayreuth, Bayreuth, Germany
Karim Benharrak
University of Bayreuth, Bayreuth, Germany
Florian Lehmann
University of Bayreuth, Bayreuth, Germany
Daniel Buschek
University of Bayreuth, Bayreuth, Germany
論文URL

https://doi.org/10.1145/3526113.3545672

会議: UIST 2022

The ACM Symposium on User Interface Software and Technology

セッション: Search and Exploration

6 件の発表
2022-11-02 23:30:00
2022-11-03 01:00:00