A dataset for exploring gaze behaviors in text summarization

Abstract

Automatic text summarization has been a hot research topic for years. Though most of the existing studies only use the content itself to generate the summaries, researchers believe that an individual's reading behaviors have much to do with the summaries s/he generates, usually regarded as the ground truth. However, such research is limited by the lack of a dataset that provides the connection between people's reading behaviors and the summaries provided by them. This paper fills in this gap by providing a dataset covering 50 individuals' gaze behaviors collected by a high-accurate eye tracking device (that generates 100 gaze points per second) when they are reading 100 articles (from 10 popular categories) and composing the corresponding summaries for each article. Collected in a controlled environment, our dataset with 157 million gaze points in total, provides not only the basic gaze behaviors when different people read an article and compose its corresponding summary, but also the connections between different behavior patterns and the summaries they will provide. We believe such a dataset will be valuable for a wide range of studies, and we also provide sample use cases of the dataset.