NDLI: Efficient keyword extraction for meaningful document perception

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Efficient keyword extraction for meaningful document perception

Content Provider	ACM Digital Library
Author	Bohne, Thomas Borghoff, Uwe M. Rönnau, Sebastian
Abstract	Keyword extraction is a common technique in the domain of information retrieval. Keywords serve as a minimalistic summary for single documents or document collections, enabling the reader to quickly perceive the main contents of a text. However, they are often not readily available for the documents of interest. Common keyword extraction techniques demand either a large data collection, a learning process, or access to extensive amounts of reference data. By relying on additional linguistic features (e.g. stop word removal), most approaches are language-restricted. Moreover, the extracted keywords usually pertain to the entire document, rather than only to the portion that is of interest to the reader. In this paper, we present an efficient and flexible approach to summarize selections of text within a document. Our solution is based on a keyword extraction algorithm that is applicable to a variety of documents, regardless of language or context. This algorithm relies on the Helmholtz principle and extends a recently presented approach. Our extension covers the features of a weighting algorithm while providing a self-regulation capability to allow for more meaningful results. Furthermore, our approach takes into account the document structure in order to enhance pure statistic summarizations. We evaluate the efficiency of our approach and present results with meaningful examples. In addition, we outline further applications of our approach that allow for enhanced document perception as well as for meaningful document indexing and retrieval.
Starting Page	185
Ending Page	194
Page Count	10
File Format	PDF
ISBN	9781450308632
DOI	10.1145/2034691.2034732
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-09-19
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Keyword extraction Single document Heuristic algorithm Information retrieval Text data mining
Content Type	Text
Resource Type	Article

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