Hammer PDF: An Intelligent PDF Reader for Scientific Papers
April 06, 2022 Β· Declared Dead Β· π International Conference on Information and Knowledge Management
"No code URL or promise found in abstract"
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Authors
Sheng-Fu Wang, Shu-Hang Liu, Tian-Yi Che, Yi-Fan Lu, Song-Xiao Yang, Heyan Huang, Xian-Ling Mao
arXiv ID
2204.02809
Category
cs.DL: Digital Libraries
Cross-listed
cs.IR
Citations
3
Venue
International Conference on Information and Knowledge Management
Last Checked
6 months ago
Abstract
It is the most important way for researchers to acquire academic progress via reading scientific papers, most of which are in PDF format. However, existing PDF Readers like Adobe Acrobat Reader and Foxit PDF Reader are usually only for reading by rendering PDF files as a whole, and do not consider the multi-granularity content understanding of a paper itself. Specifically, taking a paper as a basic and separate unit, existing PDF Readers cannot access extended information about the paper, such as corresponding videos, blogs and codes. Meanwhile, they cannot understand the academic content of a paper, such as terms, authors, and citations. To solve these problems, we introduce Hammer PDF, an intelligent PDF Reader for scientific papers. Apart from basic reading functions, Hammer PDF has the following four innovative features: (1) information extraction ability, which can locate and mark spans like terms and other entities; (2) information extension ability, which can present relevant academic content of a paper, such as citations, references, codes, videos, blogs; (3) built-in Hammer Scholar, an academic search engine based on academic information collected from major academic databases; (4) built-in Q&A bot, which can find helpful conference information. The proposed Hammer PDF Reader can help researchers, especially those studying computer science, to improve the efficiency and experience of reading scientific papers. We have released Hammer PDF, available at https://pdf.hammerscholar.net/face.
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