Automated Multi-Modal Resource Extraction for Research Papers
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Solution Overview
Problem
Researchers face a time-consuming and cumbersome process when manually searching for and extracting multi-modal online resources such as videos, slides, and program codes associated with research papers, as they need to access multiple scattered websites and verify the relevance of the content, which does not scale well for a large number of papers.
Innovation Solution
A method and system that automatically extracts and compares multi-modal online resources with research papers by identifying resource types, encoding content fields into vectors, and determining a final set of resources for display, reducing the need for manual input and effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual search and extraction methods are used, then researchers can access online resources, but the process becomes time-consuming and cumbersome
Solution Approach 1:
The system performs self-service by automatically extracting resources, identifying their types, validating content fields, and generating final sets without requiring researcher intervention. The automated workflow includes extracting resources from websites, determining resource types based on predefined categories, encoding content fields into vectors for comparison, and systematically validating relevance - all tasks that would otherwise require manual researcher effort
Solution Approach 2:
The patent replaces the mechanical manual search and validation process with an automated computational system. Instead of researchers manually browsing websites, identifying resources, and validating content, the system uses automated extraction mechanisms, type identification algorithms, vector encoding, and computational comparison to perform these tasks efficiently and at scale
2Productivity
If automated extraction is implemented, then time efficiency improves, but system complexity increases
Solution Approach 1:
The automated system is segmented into distinct functional modules: a resource extraction module that gathers online resources, a type identification module that categorizes resources based on predefined types, a content field encoding module that converts fields into vectors, and a validation module that compares vectors to determine relevance. This segmentation allows each component to perform its specific function independently, managing overall system complexity through modular design
Solution Approach 2:
The system manages complexity by transforming content fields into vector representations, changing the parameter format from raw text to structured numerical data. This parameter transformation enables systematic comparison and validation processes, making the complex task of resource verification manageable through mathematical operations rather than manual analysis
3Measurement precision
If comprehensive resource validation is performed, then resource relevance accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary action by encoding content fields into vectors during the extraction phase, before validation is needed. This pre-processing step transforms raw content into a standardized format that enables rapid comparison later, so that when validation occurs, the computationally intensive encoding work has already been completed, reducing the time required for actual relevance assessment
Data Source
AI summary
A method includes storing a research paper and a set of candidate resources including media content. The method further includes encoding each of one or more first content fields in the research paper into a first vector based on a first field type associated with each of the one or more first content fields. The method further includes encoding each of one or more second content fields in each of the parsed set of candidate resources into a second vector, based on a second field type associated with each of the one or more second content fields. The method further includes comparing the first vector with the second vector to determine a final set of resources based on the comparison. The method further includes controlling a display screen to output the determined final set of resources and the research paper.