Abstract: A system for genre-specific summarization of documents is provided that overcomes the problem of summarizing heterogeneous document collections by taking the genre, or type, of document into account when selecting summary sentences. The system of the present invention takes advantage of the structure and wording of various document genres to provide faster and more accurate summaries.

Abstract: A method and a system for generating a document summary. The method and the system extract text along with corresponding position information from a document, provide the extracted text to a document summarizer and generates a summary with indicators that indicate the corresponding locations in the document from which the summary portions were extracted.

Abstract: Systems and methods are provided for organizing text content of one or more text passages, such as text passages obtained in response to a search query, and/or other text passages, using an organization based on concept terms obtained from the one or more text passages. A hierarchical structure is used to organize the documents in a way that informs the user about co-occurrence relations among terms that represent concepts, indicating the relative degree of occurrence and context of discussion of the terms within the search results. One or more candidate hierarchies may be generated, each with a different term in the most-dominant position. The one or more candidate hierarchies can be evaluated, and a hierarchy to be displayed can be selected based on the evaluation.

Abstract: A method and a system for generating a document summary. The method and the system extract text along with corresponding position information from a document, provide the extracted text to a document summarizer and generates a summary with indicators that indicate the corresponding locations in the document from which the summary portions were extracted.

Abstract: A method of automatically generating document extracts. The method makes use of feature value probabilities generated from a statistical analysis of manually generated summaries to extract the same set of sentences an expert might. The method is based upon an iterative approach. First, the computer system designates a sentence of the document as a selected sentence. Second, the computer system determine values for the selected sentence of each feature of a feature set. Third, the computer system increases a score for the selected sentence based upon the value of the feature for the selected sentence and upon the probability associated with that value. Fourth, after scoring all of the sentences of the document the computer system, the computer system selects a subset of the highest scoring sentences to be extracted.

Abstract: A method of automatically generating feature probabilities that allow later automatic generation of document extracts. The computer system generates the probabilities by analyzing each document a document at a time. First, the computer system designates one of the documents as a selected document. Next, the computer system analyzes each sentence of the selected document to determine the value of the paragraph feature and the value of the uppercase feature. The computer system repeats this effort for each document of the document corpus. Afterward, the number of occurrences of each value of each feature is calculated and is used to calculate feature value probabilities for all of the features.

Abstract: A computerized method for retrieving documents from a text corpus in response to a user-supplied natural language input string, e.g., a question. An input string is accepted and analyzed to detect phrases therein. A series of queries based on the detected phrases is automatically constructed through a sequence of successive broadening and narrowing operations designed to generate an optimal query or queries. The queries of the series are executed to retrieve documents, which are then ranked and made available for output to the user, a storage device, or further processing. In another aspect the method is implemented in the context of a larger two-phase method, of which the first phase comprises the method of the invention and the second phase of the method comprises answer extraction.

Abstract: A computerized method for organizing information retrieval based on the content of a set of primary documents. The method generates answer hypotheses based on text found in the primary documents and, typically, a natural-language input string such as a question. The answer hypotheses can include phrases or words not present in the input string. Answer hypotheses are verified and ranked based on their verification evidence. A text corpus can be queried to provide verification evidence not present in the primary documents. In another aspect the method is implemented in the context of a larger two-phase method, of which the first phase comprises the method of the invention and the second phase of the method comprises answer extraction.

Abstract: A system and method for automatically transcribing an input question from a form convenient for user input into a form suitable for use by a computer. The question is a sequence of words represented in a form convenient for the user, such as a spoken utterance or a handwritten phrase. The question is transduced into a signal that is converted into a sequence of symbols. A set of hypotheses is generated from the sequence of symbols. The hypotheses are sequences of words represented in a form suitable for use by the computer, such as text. One or more information retrieval queries are constructed and executed to retrieve documents from a corpus (database). Retrieved documents are analyzed to produce an evaluation of the hypotheses of the set and to select one or more preferred hypotheses from the set. The preferred hypotheses are output to a display, speech synthesizer, or applications program. Additionally, retrieved documents relevant to the preferred hypotheses can be selected and output.