Abstract:
A combinatorial summarizer includes a plurality of summarization engines, a processor in selective communication with each summarization engine, and computer readable instructions executable by the processor and embodied on a tangible, non-transitory computer readable medium. Each summarization engine is to select a respective plurality of sentences, and generate a relative rank and an associated weight for each sentence of the respective plurality of sentences. The computer readable instructions include instructions to determine a combined weight for each sentence of each respective plurality of sentences. The combined weight is based upon the respective associated weight and a respective relative human rank for each sentence in a set of sentences, including all sentences of each respective plurality of sentences. The computer readable instructions further include instructions to determine a total weight for each summarization engine based, respectively, upon the combined weights for each sentence of each respective plurality of sentences.
Abstract:
One example is a system including a plurality of summarization engines, an evaluator, and a selector. Each of the plurality of summarization engines receives content to provide a summary of the content. The evaluator determines a value of each summary for a selected task. The selector selects a summarization engine for the selected task based on the assessed value of each summary.
Abstract:
Examples disclosed herein relate to an Internet Protocol address distribution summary. A processor may determine a statistical distribution between at least one portion of bits of Internet Protocol addresses accessing a website and determine a summary value representative of the degree of change within the statistical distribution. The processor may output the summary value.
Abstract:
Examples disclosed herein relate to item identifier assignment. For example, a processor may create a mass serialization scheme to provide a list of item identifiers to allow a subset of the identifiers to be knocked out. Each item identifier may associated with multiple containers, and each container may include two item identifiers relating to the sequence of item identifiers included within the container.
Abstract:
Examples herein disclose multiple engines to produce output representative of a summary of document produced by each of the multiple engines. The examples apply a weighting mechanism to the output specific to that engine to obtain a value corresponding to that output. The examples identify specialized language if the value corresponding to that output reaches at least a particular threshold.
Abstract:
One example is a system including a plurality of summarization engines, an evaluator, and a selector. Each of the plurality of summarization engines receives content to provide a summary of the content. The evaluator determines a value of each summary for a selected task. The selector selects a summarization engine for the selected task based on the assessed value of each summary.
Abstract:
One example is a system including a plurality of combinations of summarization engines and/or meta-algorithmic patterns used to combine a plurality of summarizers, an extractor, an evaluator, and a selector. Each of the plurality of combinations of summarization engines and/or meta-algorithmic patterns receives content to provide a meta-summary of the content. The extractor generates a collection of search queries based on the content. The evaluator determines a similarity value of each combination of summarization engines and/or meta-algorithmic patterns for the collection of search queries. The selector selects an optimal combination of summarization engines and/or meta-algorithmic patterns based on the similarity value.
Abstract:
An example apparatus may include a processor and a memory device including computer program code. The memory device and the computer program code may, with the processor, cause the apparatus to provide modified serialization codes for a first entity in a serialization flow to replace existing serialization codes for the first entity. In various examples, the modified serialization codes may have a representation of at least two different characters, and a number of instances of one of the characters in the representation of the modified serialization codes may be different from a number of instances of the one of the characters in the representation of the existing serialization codes. The memory device and the computer program code may further cause the apparatus to receive serialization codes from a second entity, the second entity being downstream in the serialization flow from the first entity; compare serialization codes from the second entity with the modified serialization codes; and verify the serialization codes from the second entity by determining if the serialization codes from the second entity are compatible with the modified serialization codes.
Abstract:
Functional summarization of non-textual content based on a meta-algorithmic pattern is disclosed. One example is a system including a converter, a plurality of summarization engines and/or meta-algorithmic patterns, an extractor, and an evaluator. The converter converts the non-textual content into a plurality of tokens. Combinations of summarization engines and/or meta-algorithm patterns are applied to the plurality of tokens to provide a meta-summary. The extractor extracts at least one summarization term from the meta-summary, and at least one class term for each given class of a plurality of classes of non-textual content. The evaluator determines similarity values of the non-textual content over each given class, each similarity value indicative of a similarity between the at least one summarization term and the at least one class term for each given class. The selector selects a class of the plurality of classes, the selecting based on the determined similarity values.
Abstract:
One example is a system including a plurality of combinations of summarization engines and/or meta-algorithmic patterns used to combine a plurality of summarizers, an extractor, an evaluator, and a selector. Each of the plurality of combinations of summarization engines and/or meta-algorithmic patterns receives content to provide a meta-summary of the content. The extractor generates a collection of search queries based on the content. The evaluator determines a similarity value of each combination of summarization engines and/or meta-algorithmic patterns for the collection of search queries. The selector selects an optimal combination of summarization engines and/or meta-algorithmic patterns based on the similarity value.