Difference between revisions of "Text summarization"
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== Common Approaches == | == Common Approaches == | ||
| − | Common approaches to text summarization can typically be | + | Common approaches to text summarization can typically be classified into one of the following categories: |
* '''Extraction''', extracts most important information (sentences or paragraphs) from original text and copies them to make summary | * '''Extraction''', extracts most important information (sentences or paragraphs) from original text and copies them to make summary | ||
Revision as of 17:13, 30 September 2010
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 classified 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
Evaluation
One commonly used evaluation metric in summarization is ROUGE, which is used in NIST's Document Understanding Conferences summarization tasks.
