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Issue No.11 - November (2000 vol.33)
pp: 29-36
<p>Summarization--the art of abstracting key con-tent from one or more information sources--has become an integral part of everyday life. People keep abreast of world affairs by listening to news bites. They base investment decisions on stock market updates. They go to movies largely on the basis of reviews. With summaries, they can make effective decisions in less time.</p> <p>Although summarizing tools are available, with the increasing volume of online information, it is becoming harder to generate meaningful and timely summaries. Researchers are investigating tools and methods that automatically extract or abstract content from information sources.</p> <p>The authors describe how these data summarization methods fall into two categories. Knowledge-poor approaches rely on not having to add new rules for each new application domain or language. Knowledge-rich approaches assume that if you grasp the meaning of the text, you can reduce it more effectively, thus yielding a better summary. They rely on a size-able knowledge base of rules, which must be acquired, maintained, and then adapted to new applications and languages.</p> <p>The authors predict that summarization tools will be key in conquering the vast information universes ahead.</p>
Udo Hahn, Inderjeet Mani, "The Challenges of Automatic Summarization", Computer, vol.33, no. 11, pp. 29-36, November 2000, doi:10.1109/2.881692
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