Knowledge-based Automatic Topic Identification

Knowledge-based Automatic Topic Identification Chin-Yew Lin Department of Electrical Engineering/System University of Southern California Los Angeles, CA 90089-2562, USA chinyew~pollux.usc.edu Abstract As the first step in an automated text sum- marization algorithm, this work presents a new method for automatically identi- fying 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 appropri- ate cutoff values for such parameters as concept generality and child-to-parent fre- quency ratio, we control the amount and level of generality of concepts extracted from the text. 1 1 Introduction As the amount of text available online keeps grow- ing, it becomes increasingly difficult for people to keep track of and locate the information of inter- est to them. To remedy the problem of information overload, a robust and automated text summarizer or information extrator is needed. Topic identifica- tion is one of two very important steps in the process of summarizing a text; the second step is summary text generation. A topic is a particular subject that we write about or discuss. (Sinclair et al., 1987). To identify the topics of texts, Information Retrieval (IR) re- searchers use word frequency, cue word, location, and title-keyword techniques (Paice, 1990). Among these techniques, only word frequency counting can be used robustly across different domains; the other techniques rely on stereotypical text structure or the functional structures of specific domains. Underlying the use of word frequency is the as- sumption that the more a word is used in a text, the more important it is in that text. This method 1This research was funded in part by ARPA under or- der number 8073, issued as Maryland Procurement Con- tract # MDA904-91-C-5224 and in part by the National Science Foundation Grant No. MIP 8902426. recognizes only the literal word forms and noth- ing else. Some morphological processing may help, but pronominalization and other forms of coreferen- tiality defeat simple word counting. Furthermore, straightforward word counting can be misleading since it misses conceptual generalizations. For exam- ple: "John bought some vegetables, fruit, bread, and milk." What would be the topic of this sentence? We can draw no conclusion by using word counting method; where the topic actually should be: "John bought some groceries." The problem is that word counting method misses the important concepts be- hind those words: vegetables, fruit, etc. relates to groceries at the deeper level of semantics. In rec- ognizing the inherent problem of the word counting method, recently people have started to use artifi- cial intelligence techniques (Jacobs and ttau, 1990; Mauldin, 1991) and statistical techniques (Salton et al., 1994; Grefenstette, 1994) to incorporate the sementic relations among words into their applica- tions. Following this trend, we have developed a new way to identify topics by counting concepts instead of words. 2 The Power of Generalization In order to count concept frequency, we employ a concept generalization taxonomy. Figure 1 shows a possible hierarchy for the concept digital computer. …

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