Two-Stage Encoder-Decoder for Tabular Data Text Summarization
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Solution Overview
Problem
Conventional end-to-end neural network-based techniques for generating textual summaries from tabular data struggle with encoding numeric concepts and relationships, leading to inaccurate and ineffective summary generation due to the inability to generalize to unseen numeric values and the loss of tabular structure.
Innovation Solution
A two-stage encoder-decoder network is employed, incorporating rank information based on numeric values to learn correlations and generate textual summaries, which includes a first-stage encoder-decoder network for content selection and a second-stage encoder-decoder network for natural language processing and copy generation, utilizing a pointer attention mechanism to order records based on rank.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional end-to-end neural network-based techniques are used for textual summary generation, then automation is achieved, but accuracy deteriorates due to inability to encode numeric concepts and relationships
Solution Approach 1:
The patent divides the summary generation task into two distinct stages: (i) structured content selection from tabular data, and (ii) textual summary generation from selected content. This segmentation allows each stage to be optimized independently, with the first stage focusing on accurate numeric value selection and the second stage on natural language generation, thereby resolving the contradiction between automation and accuracy.
Solution Approach 2:
The patent introduces an intermediate structured content selection stage that acts as a mediator between the input tabular data and the final textual summary. This intermediate representation preserves numeric concepts and relationships in a structured format before text generation, enabling the system to maintain accuracy while achieving full automation.
2Device complexity
If numeric data is encoded via textual embeddings in conventional techniques, then processing is simplified, but generalization to unseen numeric values becomes challenging
Solution Approach 1:
The patent changes the encoding approach for numeric data from textual embeddings to a specialized numeric encoding scheme that preserves numeric properties. This parameter change in the encoding method allows the system to maintain simplicity while achieving better generalization to unseen numeric values, as the encoding explicitly captures numeric relationships rather than treating all values as unique tokens.
3Measurement precision
If manual textual summary generation is used, then accuracy is improved, but productivity deteriorates due to time-consuming processes
Solution Approach 1:
The patent implements a self-service system where the two-stage model automatically performs both content selection and text generation without manual intervention. The system learns from training data to autonomously select relevant numeric values and generate accurate textual summaries, achieving both high accuracy and high productivity by eliminating the need for manual summary creation while maintaining quality through learned patterns.
Data Source
AI summary
This disclosure relates generally to methods and systems for generating a textual summary from a tabular data. During the textual summary generation using conventional end-to-end neural network-based techniques, a numeric data present in the tables is encoded via textual embeddings. However, the textual embeddings cannot reliably encode information about numeric concepts and relationships. The methods and systems generate the textual summary from the tabular data, by incorporating rank information for different records present in the tabular data. Then, a two-stage encoder-decoder network is used to learn correlations between the rank information and the probability of including the records based on the rank information, to obtain the textual summary generation model. The textual summary generation model identifies the content selection having the records present in the tables to be included in the textual summary and generates the textual summary from the identified content selection.