Abstractive Slide Generation via Neural Retrieval and QA
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Solution Overview
Problem
Conventional methods for generating presentation slides from source documents rely on extractive-based mechanisms, resulting in tedious and time-consuming processes, and often assume a one-to-one match between slide titles and document subtitles, failing to produce abstractive summaries that reflect the user's intended content.
Innovation Solution
An interactive two-step architecture using Dense Vector Information Retrieval and Long Form Question Answering machine learning processes to identify relevant document sections and generate abstractive summaries based on user-input titles and keywords, allowing for the creation of presentation slides with content that accurately represents the source documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If extractive-based mechanisms are used to generate presentation slides from source documents, then the process becomes automated, but the generated content is merely an aggregation of raw sentences lacking abstractive summarization quality
Solution Approach 1:
The patent replaces extractive-based mechanical text selection with neural network-based abstractive summarization. The encoder-decoder architecture transforms the generation process from simple sentence extraction to intelligent content synthesis, where the neural network learns to generate coherent summaries that capture the essence of source documents rather than merely aggregating raw sentences.
Solution Approach 2:
The patent changes the operational parameters of text generation by introducing attention mechanisms and sequence-to-sequence modeling. Instead of fixed extraction rules, the system dynamically adjusts which parts of the source text to focus on and how to rephrase them, enabling flexible abstractive summarization that adapts to different document types and presentation requirements.
2Device complexity
If conventional techniques assume one-to-one match between slide titles and document subtitles, then the process is simplified, but it fails to capture the creator's intended meaning and may require multiple slides under the same title
Solution Approach 1:
The patent segments the title matching process into multiple independent components: the encoder processes the source document and generates contextual representations, the attention mechanism identifies relevant sections, and the decoder generates appropriate slide titles. This segmentation allows the system to handle complex title-slide relationships without requiring a simple one-to-one mapping, preserving the creator's intended meaning while maintaining process manageability.
Solution Approach 2:
The patent introduces an intermediary attention mechanism that bridges the gap between document content and slide titles. Rather than directly mapping subtitles to titles, the attention mechanism acts as a mediator that selectively focuses on relevant document portions and generates titles that accurately reflect the creator's intent, even when multiple slides share the same title or when titles diverge from original subtitles.
3Manufacturing precision
If manual creation of presentation slides is performed, then the content quality and accuracy are high, but the process is tedious and time-consuming
Solution Approach 1:
The patent enables the system to perform abstractive summarization and title generation autonomously without requiring manual intervention for each slide. The neural network model processes source documents and automatically generates presentation slides with accurate content and appropriate titles, eliminating the tedious manual work while maintaining high content quality that previously required human creators.
Data Source
AI summary
Systems and methods for creating presentation slides. A slide title is received and portions of source documents relevant to the title are identified based on a dense vector information retrieval machine learning process. An abstractive summary of the portions is generated based on a long form question answering machine learning process. A first presentation slide is created with the abstractive summary and the title. The first presentation slide is presented to an operator and an input indicating one of accepting or rejection the abstractive summary is received. Based on the input that indicating rejecting the abstractive summary, the abstractive summary is removed from the presentation slide and negative training feedback for the abstractive summary is provided to at least one of the dense vector information retrieval machine learning process or the long form question answering machine learning process.