Web Content Summarization Engine with Bias Neutralization
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Solution Overview
Problem
Current internet search technologies fail to summarize information objectively and neutralize author bias in web pages and blogs, leading to users being overwhelmed with biased content and large result sets.
Innovation Solution
A system comprising multiple modules: an ontology module for parsing and tagging relevant information, a summation module for aggregating and interpreting content, and a language generation module using predefined grammar rules to create objective summaries, with a language bias module adjusting for user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If keyword-driven search technology is used to search online documents, then the search service can be provided, but the result set becomes large and users are overwhelmed with biased content
Solution Approach 1:
The patent extracts only the most relevant information from web documents by using an ontology module to identify key entities and relationships, a summation module to aggregate relevant data points, and a language generation module to synthesize objective summaries. This extraction process filters out unnecessary content while retaining essential information, resolving the contradiction between providing comprehensive search results and avoiding overwhelming users with excessive content.
2Adaptability or versatility
If web pages and blog posts are published with author's point of view, then free speech and diverse information are provided, but the content becomes biased and users cannot obtain objective interpretations
Solution Approach 1:
The patent introduces an intermediary processing system between biased source content and users. The language bias module acts as a mediator that detects biased language patterns in source documents and transforms them into neutral, objective summaries. This intermediary process preserves the diversity of information sources while eliminating authorial bias, allowing users to access multiple perspectives without being influenced by individual author viewpoints.
Solution Approach 2:
The system employs feedback mechanisms where the language bias module continuously adjusts its bias neutralization strategies based on detected patterns in source content. By analyzing the bias characteristics of different web documents and blog posts, the system adapts its processing to maintain objectivity across diverse information types, resolving the contradiction between preserving information diversity and ensuring objective presentation.
3Loss of information
If the number of online documents increases to provide comprehensive information, then more topics are covered, but keyword-driven searches provide larger result sets that are harder to navigate
Solution Approach 1:
The patent replaces the mechanical keyword-matching search system with an intelligent semantic processing system. Instead of relying on users to manually navigate through large result sets based on keyword matches, the system uses ontology-based understanding, summation algorithms, and natural language generation to automatically synthesize objective summaries of relevant information. This substitution transforms the search experience from manual navigation of extensive results to direct presentation of synthesized insights, resolving the contradiction between information completeness and navigation ease.
Data Source
AI summary
A system and method for generating textual structures describing the information contained on multiple web pages and blogs. The system comprises an ontology module, summation module, language generation module, and language bias module. The method comprises receiving a request for summarized web information, accumulating text from a plurality of web pages, parsing the accumulated text, indexing the text into a plurality of information sets, storing the plurality of information sets into a memory structure, aggregating information contained in the plurality of information sets to create a structure interpretation to satisfy the request, and creating at least one new textual structure from the structure interpretation. The ontology module parses and tags accumulated web text. The summation module creates a structure interpretation of the parsed and tagged web text. The language generation module creates textual structure describing the structure interpretation.