Multi-Document Search Summarization With Cited Consolidated Answers
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Solution Overview
Problem
Existing search technologies often require users to sift through multiple results and perform additional research to find comprehensive and accurate information, as they typically provide sub-optimal, partial, or non-responsive search results that fail to address all aspects of a user's query efficiently.
Innovation Solution
A generative artificial intelligence (GenAI) based search system that utilizes advanced machine learning models to process and summarize content from diverse sources, generating a single, coherent, and contextually aware response that encapsulates the required information, including citations, thereby streamlining the search process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional search technologies are used to retrieve multiple search results, then users can access diverse information sources, but users must spend additional time sifting through multiple results to find comprehensive and accurate information
Solution Approach 1:
The system merges multiple search result documents into a single consolidated summary that integrates information from all sources. The generative AI model processes multiple documents simultaneously and produces one unified response that combines relevant information, eliminating the need for users to manually review multiple separate results.
Solution Approach 2:
The system performs preliminary summarization of multiple search results before presenting them to the user. By pre-processing and synthesizing the information in advance, the system delivers a ready-to-use consolidated answer that requires no additional user effort to aggregate or evaluate multiple sources.
2Reliability
If traditional search technologies provide multiple search results, then users can access various sources, but the results are sub-optimal, partial, or non-responsive and fail to address all aspects of the query efficiently
Solution Approach 1:
The generative AI model incorporates feedback mechanisms to continuously improve search result quality. The system analyzes user interactions with search results and uses this feedback to refine its summarization process, ensuring that consolidated answers increasingly address all aspects of queries accurately and comprehensively.
Solution Approach 2:
The system dynamically adjusts parameters such as the number of documents processed, the depth of analysis, and the synthesis approach based on query complexity and user needs. This allows the system to optimize between thoroughness and speed, delivering reliable comprehensive answers when needed while maintaining efficiency for simpler queries.
3Measurement precision
If users manually review multiple search results to find comprehensive information, then they can verify accuracy, but the process is time-consuming and reduces user efficiency
Solution Approach 1:
The system performs self-verification by automatically cross-referencing information across multiple documents and identifying consistent facts. The generative AI model inherently validates information by comparing sources and resolving contradictions, eliminating the need for users to manually verify accuracy while maintaining high reliability.
Data Source
AI summary
Enhanced search results are generated using multi-document summarization. A multi-document summarization system receives a search query from a user and retrieves a plurality of search result documents based on the search query. The summarization system generates a summary of each of the plurality of search result documents using distinct per-document summarization machine learning models, where the distinct per-document summarization machine learning models are trained on a training dataset. The summarization system synthesizes the summary of each of the plurality of search result documents into a single-consolidated answer responsive to the received search query. The multi-document summarization system formats the single-consolidated answer to include citations to the plurality of search result documents.