Summarization-Driven Answer Extraction for Domain-Agnostic QA
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional information retrieval techniques lack the ability to fully interpret or understand documents, leading to zero outputs or incorrect answers in machine reading comprehension tasks, particularly in open-domain questioning and answering systems.
Innovation Solution
A domain-agnostic answering system utilizing a summarizer to generate summary representations with score vectors, a retriever to identify a winning candidate, a transformer to determine start and end logit vectors, and an impact matrix to adjust these vectors for precise answer extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If classical information retrieval techniques are used, then the system can find similarities between questions and answers through preprocessing, but the system lacks the ability to fully interpret or understand documents
Solution Approach 1:
The patent introduces summarizers as intermediary components that generate summary representations of documents before they are processed by the retrieval system. These summaries act as mediators between the raw documents and the question-answering system, enabling both precise matching and better document understanding without requiring full interpretation of entire documents
Solution Approach 2:
The system segments documents into summary representations that capture essential information. By dividing documents into manageable summary units with score vectors, the system can process and understand document content more effectively while maintaining retrieval precision
2Measurement precision
If the system processes entire documents to ensure accurate answer extraction, then answer precision improves, but search time and computational resources increase
Solution Approach 1:
The system performs preliminary summarization of documents before the actual question-answering process. By pre-generating summary representations and score vectors for all documents, the system prepares processed forms of documents in advance, enabling fast retrieval without processing entire documents during query time
Solution Approach 2:
The system extracts only the essential information from documents by generating summary representations. Instead of processing entire documents, the retrieval operation works with condensed summary forms that contain the most relevant information, significantly reducing search time while maintaining answer accuracy
3Measurement precision
If the system uses domain-specific training data to improve performance in particular domains, then answer accuracy in those domains improves, but the system becomes less adaptable to new domains
Solution Approach 1:
The patent implements a universal question-answering framework that can operate across multiple domains without requiring domain-specific training. The summarizer-retriever-transformer architecture processes documents from any domain using the same mechanisms, making the system adaptable to new domains while maintaining competitive performance through domain-agnostic document understanding
Data Source
AI summary
A domain-agnostic answering system configured to: (a) receive a question and one or more documents; (b) generate summary representations the one or more documents, each summary representation including a summary having one or more sentences and a score vector; (d) determine that a first summary representation of the summary representations is a winning candidate for extracting an answer to the question; (e) match the first summary representation to a first document in the one or more documents to obtain reference indexes of sentences in the first summary representation in portions of the first document; (f) determine a start logit vector and an end logit vector from the question and the matched first summary representation; and (g) generate a start span and an end span from the start logit vector, the end logit vector, and the score vector associated with the first summary representation, the start span and the end span representing the answer to the question.