Question-Answer Document Summarization via Dialog Act Ontology
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Solution Overview
Problem
Current technologies face challenges in transforming question-answer documents into declarative segments effectively, particularly in legal depositions, due to complexities in parsing, anonymization, and summarization, which hinders efficient information retrieval and summarization processes.
Innovation Solution
The development of a system that parses, anonymizes, and classifies question-answer documents to transform them into declarative segments using a pipeline of processing steps including parsing, anonymization, classification, and transformation, leveraging dialog act ontology and machine learning techniques to generate semantically equivalent and grammatically correct summaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a pipeline of processing steps (parsing, anonymization, classification, transformation) is used to transform question-answer documents into declarative segments, then the quality and semantic equivalence of generated summaries is improved, but the device complexity and processing time increase
Solution Approach 1:
The processing pipeline is divided into distinct modular stages: parsing (extracting Q&A pairs), anonymization (removing sensitive information), classification (categorizing by dialog act), and transformation (generating declarative segments). Each stage handles a specific aspect of the transformation process, improving overall quality while making the complex system more manageable and maintainable through clear separation of concerns.
2Manufacturing precision
If a pipeline of processing steps is used to transform question-answer documents into declarative segments, then the quality and semantic equivalence of generated summaries is improved, but the processing time increases
Solution Approach 1:
The parsing and anonymization steps are performed as preliminary actions before the more computationally intensive classification and transformation stages. By preparing the data structure and removing sensitive information early, the subsequent processing steps can operate more efficiently on cleaned, structured data, reducing overall processing time while maintaining high quality output.
3Productivity
If compression is applied to summarize original documents with a ratio of approximately 1.5:1, then the efficiency of downstream tasks (question answering, information retrieval) is improved, but the loss of information may increase
Solution Approach 1:
The system uses dialog act classification as an intermediary step that categorizes Q&A pairs by their communicative function (e.g., assertion, question, answer) before transformation. This intermediary classification ensures that the semantic role and intent of each segment are preserved during compression, allowing the 1.5:1 summarization ratio to maintain information fidelity while improving downstream task efficiency.
Data Source
AI summary
Described herein are systems and methods to enable generation of high-quality summaries of documents that have questions and answers. To help summarize such documents, parsing methods are disclosed that account for different document formats. Methods are disclosed to anonymize personal and identifying information present in the documents. An ontology is defined to categorize dialog acts for the questions and answers present. Classifiers are disclosed based on this ontology. Methods are also disclosed to transform a question-answer pair to a canonical form. Based on the dialog acts for the question and answer combination, transformation methods were developed that build upon each of traditional NLP, and deep learning, techniques.