Lessons Learned from Users Reading Highlighted Abstracts in a Digital Library

被引：1

作者：

Kern, Dagmar ^{[1
]}

Hienert, Daniel ^{[1
]}

Angerbauer, Katrin ^{[2
]}

Dingler, Tilman ^{[3
]}

Borlund, Pia ^{[4
]}

机构：

[1] Leibniz Inst Social Sci, GESIS, Cologne, Germany

[2] Univ Stuttgart, VISUS, Stuttgart, Germany

[3] Univ Melbourne, Melbourne, Vic, Australia

[4] Oslo Metropolitan Univ, Oslo, Norway

来源：

PROCEEDINGS OF THE 2019 CONFERENCE ON HUMAN INFORMATION INTERACTION AND RETRIEVAL (CHIIR'19) | 2019年

关键词：

highlighting; reading behavior; user study; relevance judgment; TEXT;

D O I：

10.1145/3295750.3298950

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Finding relevant documents is essential for researchers of all disciplines. We investigated an approach for supporting searchers in their relevance decision in a digital library by automatically highlighting the most important keywords in abstracts. We conducted an eye-tracking study with 25 subjects and observed very different search and reading behavior which lead to diverse results. Some of the participants liked that highlighted abstracts accelerate their relevance decision, while others found that they disturb the reading flow. What many agree on is that the quality of highlighting is crucial for trust and system credibility.

引用

页码：271 / 275

页数：5

共 18 条

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