Causal Insight Summary Generation for Text Analysis
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Solution Overview
Problem
Current text summarization techniques fail to generate causal insights effectively, leading to information overload and inefficiencies in delivering relevant information to users, as they do not focus on extracting causal relationships from large texts and cannot be customized for different user interests or entities.
Innovation Solution
A processor-implemented method and system that preprocesses text data, identifies named entities and sentiment, extracts cause-effect sentences, assigns role labels, computes scores based on entities, polarities, and events, and generates causal insight summaries tailored to user interests, using techniques like named entity recognition and sentiment analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional text summarization methods are used, then summarization can be performed, but causal insights cannot be effectively generated and information overload occurs
Solution Approach 1:
The patent extracts cause-effect relationships from the text by identifying causal connectors (e.g., 'caused by', 'resulted in', 'led to') and separating them into distinct causal components. This extraction process isolates the essential causal information from the broader text, enabling focused analysis on causal insights rather than processing all text uniformly.
Solution Approach 2:
The patent segments the summarization process into distinct stages: text preprocessing, named entity recognition, sentiment analysis, cause-effect relationship extraction, and summary generation. Each stage processes specific aspects of the text independently, allowing systematic extraction of causal information while managing computational complexity through modular processing.
2Adaptability or versatility
If abstractive summarization methods are used, then new text can be generated, but large datasets are required for training
Solution Approach 1:
The patent performs preliminary analysis by extracting named entities, sentiments, and causal relationships before generating the summary. This preliminary action prepares structured representations of the text that can be directly used for customized summaries without requiring extensive training data, as the causal structure is pre-computed from the input text itself.
Solution Approach 2:
The patent introduces intermediate representations including entity-role mappings, sentiment scores, and causal graphs that serve as mediators between the raw text and the final customized summary. These intermediaries transform the text into structured causal knowledge representations that can be adapted to different user interests without requiring retraining.
3Loss of information
If all text information is processed, then complete information is obtained, but relevant information for specific users cannot be delivered
Solution Approach 1:
The patent applies local quality by analyzing different aspects of the text with specialized techniques: named entity recognition for entity identification, sentiment analysis for emotional tone, and cause-effect extraction for causal relationships. Each aspect is processed locally with appropriate methods, allowing selective focus on relevant information based on user interests without processing the entire text uniformly.
Data Source
AI summary
Conventionally, text summarization has been rule-based method and neural network based which required large dataset for training and the summary delivered had to be assessed by user in terms of relevancy. System and method are provided by present disclosure that generate causal insight summaries wherein event of importance is detected, and it is determined why event is relevant to a user. Text description is processed for named entities recognition, polarities of sentences identified, extraction of causal effects sentences (CES) and causal relationship identification in text segments which correspond to impacting events. Named entities are then role labeled. A score is computed for named entities, polarities of sentences, causal effects sentences, causal relationships, and the impacting events. A causal insight summary is generated with overall polarity being computed/determined. A customized causal insight summary is delivered to target users based on user preferences associated with specific named entities and impacting events.