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Multi-document summarization is an automatic procedure aimed at extraction of information from multiple texts written about the same topic. The resulting summary report allows individual users, such as professional information consumers, to quickly familiarize themselves with information contained in a large cluster of documents.
The 101 could sort cards based on multiple columns. For example, if a card had multiple 3-column test scores, the 101 could be wired to sort into pocket 0 those cards with no scores over 090, into pocket 1 those with one score over 090, and so on; logic limited only by the number of relays available.
For example, the set cover problem is a special case of submodular optimization, since the set cover function is submodular. The set cover function attempts to find a subset of objects which cover a given set of concepts. For example, in document summarization, one would like the summary to cover all important and relevant concepts in the document.
Edit-tricks are most useful when multiple tables must be changed, then the time needed to develop complex edit-patterns can be applied to each table. For each table, insert an alpha-prefix on each column (making each row-token "|-" to sort as column zero, like prefix "Row124col00"), then sort into a new file, and then de-prefix the column entries.
Note, rowspan="2" and colspan="2" can be used on cells to span multiple rows and columns. Header cells are created with ! Header cell, which can be column or row headers. Data cells are created with | Data cell. A new column can be added by adding another cell to the first row.
For example, a company might wish to summarize financial data by product, by time-period, and by city to compare actual and budget expenses. Product, time, city and scenario (actual and budget) are the data's dimensions. [3] Cube is a shorthand for multidimensional dataset, given that data can have an arbitrary number of dimensions.
In descriptive statistics, summary statistics are used to summarize a set of observations, in order to communicate the largest amount of information as simply as possible. Statisticians commonly try to describe the observations in
The term decision matrix is used to describe a multiple-criteria decision analysis (MCDA) problem. An MCDA problem, where there are M alternative options and each needs to be assessed on N criteria, can be described by the decision matrix which has N rows and M columns, or M × N elements, as shown in the following table.