Automatic Abstracting

The increased production and publication of professional and scientific literature makes it necessary that abstracts are pro duced in a quick, efficient and economical way. This can be achieved by the mechanization of abstracting. With the aid of computers, extracts can be produced of all kinds of texts which are available in machine-readable form. The main problem of this procedure is how to determine the key sentences of a text, i.e., the passages that contain the most relevant information. Various methods have been developed for this purpose; the one presented here is based on the fact that in order to convey relevant information, subject terminology is used. In many cases subject terminologies are now available in machine-reada ble form too and thus can be easily applied to the automatic production of abstracts.

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Research of Text Topic Automatic Extraction Method Based on Rough Set Theory

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.268-270.1127 ◽

2011 ◽

Vol 268-270 ◽

pp. 1127-1131 ◽

Cited By ~ 1

Author(s):

Zhan Feng Sun ◽

Kong Jun Bao

Keyword(s):

Set Theory ◽

Rough Set ◽

Extraction Method ◽

Rough Set Theory ◽

Complete Information ◽

Text Clustering ◽

Automatic Extraction ◽

Similarity Matrix ◽

Topic Extraction ◽

Automatic Abstracting

On the base of researching currently popular text topic extraction technologies, a new text topic automatic abstracting method is proposed based on rough set theory and rough similarity. Firstly it separated a text into words and sentences to complete information segmentation, and then constructed a similarity matrix by computing the rough similarity between different words to realize the text clustering, finally extracted representative sentences from each class to generate the text topic. The experiment shows that the method is feasible and effective.

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