Abstract
Keywords are a simple way of describing a document, giving the reader some clues about its contents. However, sometimes they only categorize the text into a topic being more useful a summary. Keywords and abstracts are common in scientific and technical literature but most of the documents available (e.g., web pages) lack such help, so automatic keyword extraction and summarization tools are fundamental to fight against the “information overload” and improve the users’ experience. Therefore, this paper describes a new technique to obtain keyphrases and summaries from a single document. With this technique, inspired by the process of protein biosynthesis, a sort of “document DNA” can be extracted and translated into a “significance protein” which both produces a set of keyphrases and acts on the document highlighting the most relevant passages. These ideas have been implemented into a prototype, publicly available in the Web, which has obtained really promising results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Dunning, T.: Statistical identification of language. Computing Research Laboratory, New Mexico State University (1994)
Sibun, P., Reynar, J.C.: Language Identification: Examining the Issues. In: Proc. of 5th Symposium on Document Analysis and Information Retrieval, Las Vegas, USA (1996)
Cavnar, W.B., Trenkle, J.M.: N-Gram-Based Text Categorization. In: Proc. of 3rd Annual Symposium on Document Analysis and Information Retrieval, Las Vegas, USA (1994)
Slonim, N., Tishby, N.: Document clustering using word clusters via the information bottleneck method. In: Proc. of 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Athens, Greece, pp. 208–215 (2000)
Hulth, A.: Improved Automatic Keyword Extraction Given More Linguistic Knowledge. In: Proc. of the 2003 Conference on Empirical Methods in NLP, Sapporo, Japan (2003)
Matsuo, Y., Ishizuka, M.: Keyword Extraction from a Single Document using Word Co-occurrence Statistical Information. In: Proc. of the Sixteenth International FLAIRS Conference, St. Augustine, FL, USA, pp. 392–396 (2003)
Hovy, E., Lin, C.: Automated text summarization in SUMMARIST. In: Mani, I., Maybury, M. (eds.) Advances in Automated Text Summarization, MIT Press, Cambridge (1999)
Srinivasan, R., Rose, G.D.: Ab initio prediction of protein structure using LINUS. Proteins: Structure, Function, and Genetics 47(4), 489–495 (2002)
Larson, S.M., et al.: Folding@Home and Genome@Home: Using distributed computing to tackle previously intractable problems in computational biology. In: Grant, R. (ed.) Computational Genomics, Horizon Press (2003)
Ferreira da Silva, J., Pereira Lopes, G.: Extracting Multiword Terms from Document Collections. In: Proc. of VExTAL, Venice, Italy (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gayo-Avello, D., Álvarez-Gutiérrez, D., Gayo-Avello, J. (2004). Naïve Algorithms for Keyphrase Extraction and Text Summarization from a Single Document Inspired by the Protein Biosynthesis Process. In: Ijspeert, A.J., Murata, M., Wakamiya, N. (eds) Biologically Inspired Approaches to Advanced Information Technology. BioADIT 2004. Lecture Notes in Computer Science, vol 3141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27835-1_32
Download citation
DOI: https://doi.org/10.1007/978-3-540-27835-1_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23339-8
Online ISBN: 978-3-540-27835-1
eBook Packages: Springer Book Archive