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Number of found records: 30

Author

HITCHCOCK, J.A.
Title
Copernic Summarizer
Source
Link Up, 2002, vol. 19, n. 2,
Support
On line (12/05/2005)
Abstract
Copernic Summarizer (http://www.copernic.com/products/summarizer) is a software product designed to facilitate the creation of a summary or condensed version of files on a personal computer (PC) or saving of Web sites on the computer for further use or transmission by printing or electronic mail. The main features of the software are described together with some practical examples of its application to a text document and to text from a Web site. (AU)
Keywords
Automatic text analysis; Automatic abstracting; Software; Copernic Summarizer
Assessment

Author

LALMAS, Mounia
Title
A model for representing and retrieving heterogeneous structured documents based on evidential reasoning
Support
PDF
Abstract
Documents often display an internal structure; they are composed of components. For example, a journal contains several articles, which themselves contain paragraphs, tables, etc. With structured documents, the retrievable units should be the document components as well as the whole document. The components of a structured document can be of different types: various media, located in a number of sites, or written in several languages. An information retrieval model for heterogeneous structured must take into account this disparity among document components. We present a model for representing and retrieving heterogeneous structured documents, that is multimedia, distributed and multilingual documents. The model is based on evidential reasoning, a formal theory that allows for the representation and the combination of knowledge. Here, knowledge is the content of document components. We show that the model provides for an appropriate representation and retrieval of heterogeneous structured documents (AU)
Keywords
structured document; components; information retrieval model; representation of heterogeneous structured document
Assessment

Author

MARCU, Daniel
Title
To build text summaries of high quality, nuclearity is not sufficient
Support
PDF
Abstract
Researchers in discourse have long hypothesized that the nuclei of a rhetorical structure tree provide a good summary of the text for which that tree was built. In this paper, I discuss a psycholinguistic experiment that validates this hypothesis, but that also shows that the distinction between nuclei and satellites is not sufficient if we want to build summaries of very high quality. I empirically compare various techniques for mapping discourse trees into partial orders that reflect the importance of the elementary textual units in texts and I discuss both their strengths and weaknesses. (AU)
Keywords
rhetorical structure; psycholinguistic experiment; textual unit
Assessment

Author

SHAW, W.M.
Title
An investigation of document structures
Source
Information Processing and Management, 1990, vol. 26, n. 3, pp.339-348
Support
On line (12/05/2005)
Abstract
Investigates the presence of clustering structure in a document collection and the influence of the presence of clustering structure on the sucess of cluster-based retrieval as a function of term-weight and similarity thresholds. The term-weight threshold selects a particular level of indexing exhaustivity for the document representation, and the similarity threshold selects a specific level of the associated single-link hierarchy. Results show clear evidence for clustering structure in the most exhaustive and the least exhaustive subject representations, and that observed values of cluster-bases retrieval effectiveness at all exhaustivity levels can be explained by assuming that the pairwise associations responsible for the structure imposed on the document collection are generated randomly. Results suggest that the structure imposed on a small document collection by an automatically produced subject representation is unrelated to the structure imposed on the documents by relevance relationships. (AU)
Keywords
Information storage and retrieval; Subject indexing; Searching; Clustering
Assessment
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