TY - GEN
T1 - Hammer PDF
T2 - 31st ACM International Conference on Information and Knowledge Management, CIKM 2022
AU - Wang, Sheng Fu
AU - Liu, Shu Hang
AU - Che, Tian Yi
AU - Lu, Yi Fan
AU - Yang, Song Xiao
AU - Huang, Heyan
AU - Mao, Xian Ling
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/10/17
Y1 - 2022/10/17
N2 - 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, etc; (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.
AB - 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, etc; (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.
KW - PDF reader
KW - information extraction
KW - literature search
UR - http://www.scopus.com/inward/record.url?scp=85140839368&partnerID=8YFLogxK
U2 - 10.1145/3511808.3557169
DO - 10.1145/3511808.3557169
M3 - Conference contribution
AN - SCOPUS:85140839368
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 5019
EP - 5023
BT - CIKM 2022 - Proceedings of the 31st ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
Y2 - 17 October 2022 through 21 October 2022
ER -