SummarFlex: Exploring Personalized News Filtering and Reading with Query-Focused Hierarchical Summarization
Pages 216 - 222
Abstract
News readers often need to filter articles of their interests and seek needed information in the selected article. However, they may face clickbait titles and redundant content, which could reduce their news filtering efficiency and reading experience. In this paper, we develop a technical prototype SummarFlex to explore news filtering and reading with query-focused hierarchical summarization. In SummarFlex, users can filter articles with titles generated by GPT that are relevant to the queried keywords. When reading a selected article, users can specify keywords to generate a highlevel summary of the full content and click a sentence to get an expanded summary hierarchically. A within-subject study (N = 20) shows that compared to a baseline tool without the summarization features, SummarFlex significantly improves users' efficiency in finding interested articles and comprehension of needed information in the article. We conclude with design insights for supporting news filtering and reading with machine-generated content.
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Index Terms
- SummarFlex: Exploring Personalized News Filtering and Reading with Query-Focused Hierarchical Summarization
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Published In
November 2024
755 pages
ISBN:9798400711145
DOI:10.1145/3678884
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- Marisol Wong-Villacrés
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Published: 13 November 2024
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- Young Scientists Fund of the National Natural Science Foundation of China
- General Projects Fund of the Natural Science Foundation of Guangdong Province in China
- National Natural Science Foundation of China
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