By Topic

The challenges of automatic summarization

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

The purchase and pricing options are temporarily unavailable. Please try again later.
2 Author(s)
Hahn, U. ; Dept. of Natural Language Process., Albert-Ludwigs-Univ., Freiburg, Germany ; Mani, I.

Summarization, the art of abstracting key content from one or more information sources, has become an integral part of everyday life. Researchers are investigating summarization tools and methods that automatically extract or abstract content from a range of information sources, including multimedia. Researchers are looking at approaches which roughly 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. Some approaches use a hybrid. In both methods, the main constraint is the compression requirement. High reduction rates pose a challenge because they are hard to attain without a reasonable amount of background knowledge. Another challenge is how to evaluate summarizers. If you are to trust that the summary is indeed a reliable substitute for the source, you must be confident that it does in fact reflect what is relevant in that source. Hence, methods for creating and evaluating summaries must complement each other.

Published in:

Computer  (Volume:33 ,  Issue: 11 )