AI Text Generation Using Argument Alignment for Event Summaries
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Solution Overview
Problem
Challenges exist in generating and managing opinion content across various forms of media, particularly in producing factually accurate and argumentatively sound summaries of events using artificial intelligence techniques.
Innovation Solution
The use of artificial intelligence techniques for simulating events, identifying historical text sources, extracting argument theses, determining alignment, and generating abstractive summaries that align with selected or alternate argumentative positions, facilitated by models like BERT-based transformers and Monte Carlo simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated text generation is implemented using AI techniques, then productivity is improved, but manufacturing precision deteriorates
Solution Approach 1:
The system performs preliminary actions by simulating events and identifying historical text sources before generating the final text. This allows the AI to pre-process and align content with argument theses, ensuring factual accuracy and argumentative soundness are established before the actual content creation, thus maintaining precision while improving productivity
Solution Approach 2:
The system determines measures of alignment between simulated textual descriptions and content related to argument theses. This feedback mechanism allows the AI to evaluate and adjust its generated text to ensure it meets standards of factual accuracy and argumentative quality, resolving the contradiction between speed and precision
2Reliability
If multiple historical text sources are analyzed for argument theses, then reliability is improved, but device complexity increases
Solution Approach 1:
The system extracts specific argument theses from historical text sources rather than processing entire texts. This extraction approach maintains reliability by focusing on key argumentative elements while reducing device complexity by eliminating the need to process and analyze every detail of multiple historical sources
Solution Approach 2:
The system changes parameters by determining measures of alignment between simulated descriptions and argument thesis content. This parameter-based approach allows the complex processing of multiple historical sources to be managed through quantifiable alignment metrics, maintaining reliability while making the system more tractable
Data Source
AI summary
Methods, systems, and computer program products for automated text generation using artificial intelligence techniques are provided herein. A computer-implemented method includes simulating, using artificial intelligence techniques, at least one textual description of at least one event related to an input event; identifying historical text sources related to the input event based on processing the at least one simulated textual description; identifying content related to argument theses from at least a portion of the historical text sources; determining at least one measure of alignment between at least a portion of the at least one simulated textual description and at least a portion of the content related to argument theses; and automatically generating text describing the input event based on the at least one measure of alignment.