Difference between revisions of "Text summarization"
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== Example Systems == | == Example Systems == | ||
| − | * | + | * [http://clair.si.umich.edu/clair/lexrank/ LexRank ] |
| − | * | + | * [http://www.clsp.jhu.edu/ws2001/groups/asmd/ MEAD summarizer] |
| − | * | + | * [http://www.newsinessence.com News In Essence] |
== References / Links == | == References / Links == | ||
* A bit outdated website with some references related to text summarization - [http://www.summarization.com/] | * A bit outdated website with some references related to text summarization - [http://www.summarization.com/] | ||
* Wikipedia article on automatic summarization - [http://en.wikipedia.org/wiki/Automatic_summarization] | * Wikipedia article on automatic summarization - [http://en.wikipedia.org/wiki/Automatic_summarization] | ||
Revision as of 15:57, 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
Some common approaches to text summarization include the following:
- 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
