User-focused, ontological, automatic text summarization using a bi-directional neural network for selecting answers based on their uncommon words to user queries

An ontology-based text summarization system, comprising:

• input interface circuitry;
• output interface circuitry;
• non-transitory storage circuitry to store one or more machine-readable instruction sets;
• neural network circuitry configured to perform Name Entity Recognition and Semantic Relation;
• a long short-term memory circuitry;
• and
• processor circuitry communicatively coupled to the input interface circuitry, the output interface circuitry, and to the non-transitory storage circuitry, the processor circuitry to:
• extract concepts and relations from a plurality of information sources that are related to a topic based on an ontology;
• determine a relevancy of each of the information sources based on a comparison of the concepts included in each of the information sources;
• receive a plurality of user queries;
• determine a relationship between one or more of the concepts and the plurality of user queries;
• extract, using the neural network circuitry, one or more sentences containing relevant information from the plurality of information sources based on the determined relationship between the concepts and the plurality of user queries;
• generate a score that includes a sum of term-frequency values for uncommon words included in each sentence identified as relevant;
• and
• generate a summary of the relevant information, wherein a sentence with a highest score is selected as an answer to each user query.

A non-transitory machine-readable storage medium that includes instructions that, when executed by processor circuitry, cause the processor circuitry to:

• extract concepts and relations from a plurality of information sources that are related to a topic based on an ontology;
• determine a relevancy of each of the information sources based on a comparison of the concepts included in each of the information sources;
• receive a plurality of user queries;
• determine a relationship between one or more of the concepts and the plurality of user queries;
• extract, using the neural network circuitry, one or more sentences containing relevant information from the plurality of information sources based on the determined relationship between the concepts and the plurality of user queries;
• generate a score that includes a sum of term-frequency values for uncommon words included in each sentence identified as relevant;
• and
• generate a summary of the relevant information, wherein a sentence with a highest score is selected as an answer to each user query.

An ontology-based automatic text summarization method, comprising:

• extracting, by processor circuitry, concepts and relations from a plurality of information sources that are related to a topic based on an ontology;
• determining, by the processor circuitry, a relevancy of each of the information sources based on a comparison of the concepts included in each of the information sources;
• receiving, by the processor circuitry, a plurality of user queries;
• determining, by the processor circuitry, a relationship between one or more of the concepts and the plurality of user queries;
• extracting, using neural network circuitry, one or more sentences containing relevant information from the plurality of information sources based on the determined relationship between the concepts and the plurality of user queries;
• generating, by the processor circuitry, a score that includes a sum of term-frequency values for uncommon words included in each sentence identified as relevant;
• and
• generating, by the processor circuitry, a summary of the relevant information, wherein a sentence with a highest score is selected as an answer to each user query.