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(Russian, svodka), in statistics, the scientific processing of the raw data statistical agencies receive in the form of reports from socialist enterprises (seeREPORTING) and as a result of statistical surveys. The principal method used in summarization is grouping. The results of the summarization are presented in statistical tables. Summarization permits the systematization and generalization of information on all the units of observation considered. In addition, summarization makes it possible to obtain the composite indexes that are needed in planning and managing the national economy.

In Soviet statistics, the principles of Marxist-Leninist theory provide the scientific basis for summarization. V. I. Lenin called attention several times to the importance and complexity of the question of the methods used to summarize raw data (see Razvitie kapitalizma v Rossii [The Development of Capitalism in Russia], in Poln. sobr. sock, 5th ed., vol. 3, pp. 119, 120, and 140; “Kapitalisticheskii stroi sovremennogo zemledeliia” [The Capitalist System of Modern Agriculture], ibid., vol. 19, p. 326; and Novye dannye o zakonakh razvitiia kapitalizma v zemledelii [New Data on the Laws Governing the Development of Capitalism in Agriculture], ibid., vol. 27, pp. 182 and 190).

The bulk of the raw data is summarized at the computer stations and computer centers of the Central Statistical Board of the USSR. The organization of the summarization of statistical data will enter a qualitatively new stage with the creation of a national automated system for the collection and processing of information used in accounting, planning, and management in the national economy. A major functional link in this system will be an automated system of state statistics (see Materialy XXIV s”ezda KPSS [Materials of the Twenty-fourth Congress of the CPSU], 1972, p. 298).


References in periodicals archive ?
In the summarization phase, a schema filters semantic predications extracted from MEDLINE citations according to a user-selected point of view and topic concept [12].
The stage of interpretation is what distinguishes extract-type summarization systems from abstract-type systems.
The main contribution of this work is proposing an unsupervised approach for extractive single-document summarization that combines the strength of word embedding with the strength of traditional weighting schemes such as Augment Weight (AW) and Entropy Frequency (EF).
Traditional linguistic data summarization techniques do not consider the neutrality on data.
Because summarization is so important for reading comprehension, many researchers have designed various learning activities and scaffolds, so that students can write high-quality summaries (Konuk, Oren, Benzer, & Sefer, 2016).
In another study, Ercan [6] presented an extractive summarization system focusing on important sentence and key phrase identification.
Fujitsu created a model by applying machine learning and natural language processing to a total of about 2,500 sets of past articles from The Shinano Mainichi Shimbun and the summaries of those articles that had been made by hand, and built an automated article summarization system that automatically creates summarized articles optimized for the cable TV news delivery service.
Sentence extraction is a technique used for automatic summarization of a passage.
To operationalize the final Saliency phase, the summarization software in this study used a statistical algorithm known as Combo.
The preface--one short paragraph followed by verbatim reproduction of the chapter abstracts--lacks analysis and summarization by the editors.
The company said that the allegations relate to infringements of MONKEYmedia patents relating to user interfaces for document summarization, RSS readers and video players that can display multiple versions of text and/or audiovisual content.
After this, we discuss some popular application areas like information retrieval, question answering, text summarization and text generation.