Sequence-to-Sequence Model for Landing Page Summary Generation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for generating supplemental content items for landing pages are either expensive and labor-intensive when manually created or produce inadequate results when generated using templates, often resulting in semantically or syntactically incorrect content that fails to attract user clicks effectively.
Innovation Solution
A computer-implemented model is trained using manually generated landing page/supplemental content item pairs to construct supplemental content items that mimic human expertise, incorporating a sequence-to-sequence model with encoders and decoders to create titles and bodies that optimize click-through rates by considering user query keywords.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If supplemental content items are generated manually, then the quality and accuracy of the content is improved, but the cost and time required increases significantly
Solution Approach 1:
The system uses template-based generation to create supplemental content items by copying and adapting pre-defined structures. Templates contain standardized formats for different content types (product features, specifications, benefits) that can be automatically filled with product data, achieving consistent quality without manual creation for each item.
Solution Approach 2:
The system dynamically adjusts template parameters based on product attributes and context. By changing parameters such as tone, detail level, and structure based on product category and target audience, the system generates high-quality content that adapts to different scenarios without requiring manual customization for each item.
2Productivity
If templates are used to generate supplemental content items, then the generation speed and efficiency is improved, but the accuracy and descriptiveness of the content deteriorates
Solution Approach 1:
The template system is made dynamic by allowing automatic selection and customization of templates based on product attributes. The system dynamically determines which template to use and how to fill it based on real-time analysis of product data, ensuring accurate and descriptive content generation without sacrificing speed.
Solution Approach 2:
The system incorporates feedback mechanisms where generated content is evaluated against quality criteria and product data accuracy requirements. This feedback loop allows the system to adjust template selection and parameter settings to improve content accuracy while maintaining automated generation efficiency.
3Adaptability or versatility
If the number of landing pages increases, then the coverage and variety of products/services is improved, but the cost of manual content generation increases proportionally
Solution Approach 1:
The system creates universal templates that can be applied across multiple landing pages and product categories. A single template framework serves multiple functions by adapting to different product types, industries, and content requirements, allowing the system to handle large numbers of landing pages with a standardized approach that reduces per-item costs.
Solution Approach 2:
The content generation process is segmented into modular components: template selection, data extraction, content assembly, and quality validation. This segmentation allows the system to efficiently process large volumes of landing pages by handling each step independently and automatically, scaling to accommodate increased numbers of products and services without proportional cost increases.
Data Source
AI summary
Described herein are technologies related to constructing supplemental content items that summarize electronic landing pages. A sequence to sequence model that is configured to construct supplemental content items is trained based upon a corpus of electronic landing pages and supplemental content items that have been constructed by domain experts, wherein each landing page has a respective supplemental content item assigned thereto. The sequence to sequence model is additionally trained using self critical sequence training, where estimated click through rates of supplemental content items generated by the sequence to sequence model are employed to train the sequence to sequence model.