Using text summarization models to improve digital reading of scientific papers

This paper presents an evaluation and comparison of three state-of-the-art models for text summarization, and proposes a new digital reading interface designed for neophyte users to exploit these models, as well as automatic keyword extraction, with little or no programming experience.

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Keywords

Paper, keyword extraction, Humanities computing, qualitative evaluation, text summarization, Interface design, Poster, digital reading, and analysis, artificial intelligence and machine learning, development

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