Abstract
Accessibility of digital libraries and other web-based repositories has caused the illusion of accessibility of the full texts of scientific papers. However, in the majority of cases such an access (at least free access) is limited only to abstracts having no more then 50–100 words. Traditional keyword-based approach for clustering this type of documents gives unstable and imprecise results. We show that they can be easy improved with more adequate keyword selection and document similarity evaluation. We suggest simple procedures for this. We evaluate our approach on the data from two international conferences. One of our conclusions is the suggestion for the digital libraries and other repositories to provide document images of full texts of the papers along with their abstracts for open access via Internet.
Work done under partial support of Mexican Government (CONACyT, SNI, CGPI, COFAA) and Korean Government (KIPA professorship). The third author is currently on Sabbatical leave at Chung-Ang University.
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Makagonov, P., Alexandrov, M., Gelbukh, A. (2004). Clustering Abstracts Instead of Full Texts. In: Sojka, P., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2004. Lecture Notes in Computer Science(), vol 3206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30120-2_17
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DOI: https://doi.org/10.1007/978-3-540-30120-2_17
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