Automatic Summarization
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Automatic Summarization is the creation of a shortened version of a text by a computer program. The product of this procedure still contains the most important points of the original text.
Access to coherent and correctly-developed text summaries can be of great use, especially in our time of information overload, in which the amount of information electronically available to us, grows every day. A good example of the use of summarization technology could be search engines such as Google.
Technologies that can make a coherent summary, of any kind of text, need to take into account several variables such as length, writing-style and syntax to make a useful summary.
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Extraction and abstraction
Broadly, one distinguishes two approaches: extraction and abstraction.
Extraction techniques merely copy the information deemed most important by the system to the summary, while abstraction involves paraphrasing sections of the source document. In general, abstraction can condense a text more strongly than extraction, but the programs that can do this are harder to develop.
A good example of the use of summarization technology could be search engines such as Google.
A real and online demo
You can summarize automatically and dynamically a Google hit list by
launching a query via http://www.pertinence.net/google/en
and then clicking "summary" (to the right of "Cached" and "Similar Pages"). Enter the login "google" and password "google" (you will not have to type them later).
Pertinence Summarizer's automatic summarization functionality may be integrated into any other search engine over the Internet or an Intranet.
It has practical features: - For all the terms of the query, the corresponding occurrences are highlighted and you can navigate between occurrences with the Tab key. - You can summarize the content of the text retrieved by Google, by a percentage selected from the orange bar (supported formats: MSWord,PDF, txt, html…)
The same function is available for French documents at http://www.pertinence.net/google/fr
Regards,
A. Lehmam The Pertinence Mining team http://www.pertinence.net/index_en.html
Types of summaries
There are different types of summaries depending what the summarization program focuses on to make the summary of the text, for example sentence extraction summaries, generic summaries or query relevant summaries. Nowadays, summarization systems are able to create both query relevant text summaries or generic machine-generated summaries depending on what the user needs. Summarization of multimedia documents, e.g. pictures or movies are also possible.
Aided summarization
Machine learning techniques from closely related fields such as Information Retrieval or Text mining have been successfully adapted to help automatic summarization.
Apart from Fully Automated Summarizers (FAS), there are systems that aid users with the task of summarization (MAHS = Machine Aided Human Summarization), for example by highlighting candidate passages to be included in the summary, and there are systems that depend on post-processing by a human (HAMS = Human Aided Machine Summarization).
Further Reading
Endres-Niggemeyer, Brigitte (1998): Summarizing Information (ISBN 3540637354) Mani, Inderjeet (2001): Automatic Summarization (ISBN 1588110605)
External link
Discover Pertinence Summarizer online : http://www.pertinence.net/ps/index.jsp?ui.lang=en