To extend the SDC and MEDOS framework to handle a larger number of reviews and additional sources for a more comprehensive product summary, several strategies can be implemented:
Scalability: Implement mechanisms to handle a larger volume of reviews by optimizing the data processing pipeline and model architecture. This may involve parallel processing, distributed computing, or utilizing cloud resources for efficient data handling.
Dynamic Selection: Develop algorithms that dynamically select the most relevant reviews and additional sources based on the context of the product. This can involve advanced natural language processing techniques to identify key information and prioritize sources accordingly.
Multi-Modal Integration: Incorporate other modalities such as images, videos, or user-generated content to provide a richer context for the product summary. This can enhance the comprehensiveness of the summary by including diverse sources of information.
Hierarchical Summarization: Implement a hierarchical summarization approach where the model first generates summaries for individual reviews and additional sources, and then combines these summaries to create a comprehensive product summary. This can help in maintaining coherence and relevance across multiple sources.
Fine-Tuning and Transfer Learning: Fine-tune the models on a diverse range of products and sources to improve generalization and adaptability. Transfer learning techniques can also be employed to leverage knowledge from one domain to another.
By incorporating these strategies, the SDC and MEDOS framework can be extended to handle a larger number of reviews and additional sources, resulting in more comprehensive and informative product summaries.