4,124 research outputs found
Generating Concise and Readable Summaries of XML Documents
XML has become the de-facto standard for data representation and exchange,
resulting in large scale repositories and warehouses of XML data. In order for
users to understand and explore these large collections, a summarized, bird's
eye view of the available data is a necessity. In this paper, we are interested
in semantic XML document summaries which present the "important" information
available in an XML document to the user. In the best case, such a summary is a
concise replacement for the original document itself. At the other extreme, it
should at least help the user make an informed choice as to the relevance of
the document to his needs. In this paper, we address the two main issues which
arise in producing such meaningful and concise summaries: i) which tags or text
units are important and should be included in the summary, ii) how to generate
summaries of different sizes.%for different memory budgets. We conduct user
studies with different real-life datasets and show that our methods are useful
and effective in practice
An approach to graph-based analysis of textual documents
In this paper a new graph-based model is proposed for the representation of textual documents. Graph-structures are obtained from textual documents by making use of the well-known Part-Of-Speech (POS) tagging technique. More specifically, a simple rule-based (re) classifier is used to map each tag onto graph vertices and edges. As a result, a decomposition of textual documents is obtained where tokens are automatically parsed and attached to either a vertex or an edge. It is shown how textual documents can be aggregated through their graph-structures and finally, it is shown how vertex-ranking methods can be used to find relevant tokens.(1)
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