WikiSeer Tackles Semantic Summaries

A new article out of WikiSeer reports that "the Santa Clara based start-up pioneering real-time semantic summarization, today announced that it has successfully tested, training and completed its 1.0 platform update using more than 3.5 million English-based articles available on the portal as well as from thousands of additional websites. In real-time WikiSeer captures the essence and core principles from any text document by extracting the five most instructive and informative sentences from a page, link or article. In the course of using Wikipedia there were thousands of articles (topics) whereby the platform would cull through tens of pages and paragraphs to arrive at the five most important sentences (user definable up to 10) with better than 85% accuracy based on user testing and feedback."

The article continues, "According to Sameer Yami, Co-founder and CEO the testing included more than 3.5 million articles representing nearly 90% of all the articles found on the English site. 'Our exhaustive development and training has allowed WikiSeer to improve its algorithm to better select and extract the core essence from any type of article – as diverse and complicated as technical essays, patent applications, and legal agreements to general articles and all points in between with great accuracy' said Mr. Yami."

Learn more and apply here.

Image: Courtesy WikiSeer