Difference between revisions of "Text summarization"
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Some major challenges in text summarization | Some major challenges in text summarization | ||
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| + | == Evaluation == | ||
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| + | One commonly used evaluation metric in summarization is ROUGE, which is used in NIST's Document Understanding Conferences summarization tasks. | ||
== Example Systems == | == Example Systems == | ||
Revision as of 14:59, 30 September 2010
Contents
Summary
Text Summarization (also known as summarization, and automatic summarization) is a natural language processing task which focuses on creating shortened versions of texts with computer algorithms/software that retain the important points of the original piece of text.
Common Approaches
Common approaches to text summarization can typically be broken down into one of the following categories:
- Extraction, extracts most important information (sentences or paragraphs) from original text and copies them to make summary
- Abstraction, paraphrases sections in the original text and relies on language generation to make the summaries coherent
Challenges / Issues
Some major challenges in text summarization
Evaluation
One commonly used evaluation metric in summarization is ROUGE, which is used in NIST's Document Understanding Conferences summarization tasks.
