Collaborative Reading Tool with NLP Summarization
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Solution Overview
Problem
Users face difficulties in reading, comprehending, and retaining information from longer documents due to the rise of small, fragmented information sources, necessitating improved systems and methods for reading assistance and summarization.
Innovation Solution
A collaborative reading tool employing natural language processing (NLP) models to provide personalized reading guidance through features like time filters, question filters, focus mode, and human-AI summarization, which highlights important sections, tracks user interactions, and generates summaries based on dwell time and user inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional summarization systems generate multiple summary iterations to improve accuracy, then summary quality is improved, but computational time and resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing the document to identify and extract key sentences and important information before summarization is actually needed. This pre-extraction of essential content allows the summarization to be generated quickly without requiring multiple iterative passes, thus resolving the contradiction between accuracy and computational time.
Solution Approach 2:
The system employs self-service mechanisms where the summarization model learns from user interactions and automatically improves its output without requiring multiple manual iterations. The system adapts to user preferences and reading patterns, generating increasingly accurate summaries autonomously, thereby reducing the need for repeated computational iterations.
2Loss of information
If the system provides comprehensive summarization of entire documents, then information completeness is improved, but user comprehension and retention of key information deteriorates
Solution Approach 1:
The system segments the document into distinct sections and identifies key sentences within each segment. Rather than presenting a single comprehensive summary, it breaks down information into manageable chunks that are easier for users to process and retain, while still maintaining overall information completeness through strategic selection of important content from each segment.
Solution Approach 2:
The system extracts only the most essential and important sentences from the document, removing non-essential information. This extraction approach maintains information completeness by focusing on key content while improving user comprehension by eliminating extraneous details that would otherwise overwhelm the reader.
3Loss of information
If the system generates detailed summaries with extensive information, then information depth is improved, but ease of reading and processing deteriorates
Solution Approach 1:
The system applies local quality by providing different levels of summarization detail in different parts of the interface. Important sections receive more detailed treatment while less critical areas are summarized more concisely. This allows the system to maintain information depth where needed while preserving reading ease overall, as users can focus on detailed sections without being overwhelmed by uniform detail throughout.
Data Source
AI summary
Embodiments described herein provide methods and systems for presenting a document and generating a human-AI summary. A system provides a user with a selection of an amount of time to spend reading the document, or a list of questions from which the user may select which questions they would like answered by reading the document. The system highlights sections of the document according to the user selection. Implicit and explicit user data such as dwell times, user highlights, and user notes, are collected while displaying the document. A human-AI summary is generated based on the document and the user data.