Microsoft Technology Licensing, LLC

Oana Nicolov

Qifa Ke

Srinivas Vadrevu

Gianluca Donato

Gang Luo

Yiping Zhou

Qi Liu

Aparna Rajaraman

Krishna Sravanthi Rajanala Sai

Dario Bigongiari

Architecture that enables the extraction of document-specific salient terms from documents for use improving the result summaries on a search engine result page (SERP), and methods to extract the salient terms from the documents using search engine logs, document metadata, and other algorithms. Document-specific salient terms can provide additional information and significantly improve user success in finding relevant documents while disregarding non-relevant documents. The architecture also enables the extraction of entity information from a variety of sources, some of which are at a query level, and other sources that are specific to a single document. All the entities available are aggregated for a set of results and the most relevant results are identified. The final set of results is then used to determine where in the document summary to apply visual emphasis or cues (e.g., bolding).

Salient terms and entities for caption generation and presentation

A summary of a document is generated in near real time. In aspects, an indication to summarize the document is received and the document is processed to generate a summary. For instance, processing includes extracting sentences from the document and generating a plurality of candidate passages from the extracted sentences. Features are extracted from each of the plurality of candidate passages and each candidate passage is ranked based at least in part on the extracted features. High-ranking candidate passages are considered likely to be important and/or representative of the document. A summary of the document is generated including one or more of the high-ranking candidate passages. The summary includes portions of the document that are considered important and/or representative of the document, so a user may review the summary in lieu of reading the entire document.

Automatic document summarization using search engine intelligence

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Sunnyvale, CA, United States of America

Oana Nicolov