Knowledge-based automatic topic identification

Authors:
Chin-Yew Lin
Affiliations:
University of Southern California, Los Angeles, CA
Venue:
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Year:
1995

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Abstract

As the first step in an automated text summarization algorithm, this work presents a new method for automatically identifying the central ideas in a text based on a knowledge-based concept counting paradigm. To represent and generalize concepts, we use the hierarchical concept taxonomy WordNet. By setting appropriate cutoff values for such parameters as concept generality and child-to-parent frequency ratio, we control the amount and level of generality of concepts extracted from the text.