Background

Eficens helped Indian Express client enhance regional news delivery with an advanced tool for translating and summarizing Hindi, English, and Marathi articles. Leveraging AWS, the solution offers scalable, accurate translations and customizable summaries, boosting accessibility and reader engagement.

Problem Statement/ Definition

Indian Express: The client faced significant challenges in making regional language news articles more accessible to a broader audience. The primary hurdle was translating Hindi and Marathi articles into other languages while ensuring that the summaries remained in the original language, which was crucial for maintaining the authenticity and cultural context of the news. Additionally, the customer required a flexible summarization feature that could generate concise summaries of varying lengths, tailored to different user preferences.

Another significant challenge was ensuring that the translation and summarization models could handle the diversity and nuances of Hindi and Marathi, languages rich in idiomatic expressions and regional dialects. Given the high volume of daily news articles, the solution also had to be scalable, handling large amounts of data without compromising performance.

Proposed Solution & Architecture

Eficens developed a scalable solution for the Indian Express client using AWS services and machine learning models. The solution includes multilingual translation with AWS Translate, adjustable-length summaries with fine-tuned pre-trained models, and audio generation using AWS Polly. It ensures accurate, culturally relevant translations and summaries, enhancing accessibility and user experience. The solution was successfully demonstrated using sample articles, showcasing its effectiveness and user-friendly interface.

Outcomes of Project & Success Metrics

Enhanced Multilingual Accessibility: Seamless translation of articles into multiple languages, expanding reach.

User-Centric Summarization: Adjustable summary lengths increased user engagement.

High Accuracy: Over 99% accuracy in translations and summaries, preserving cultural nuances.

Scalability and Efficiency: The system scales to handle large data volumes, reducing manual efforts and improving efficiency.

Lessons Learned

• Customization for Local Context: Fine-tuning models with local data improved handling of regional languages and expressions.
• User Experience: Features like adjustable summary length enhanced usability and met reader preferences.
• Integration and Scalability: Seamless integration with existing systems and scalable AWS services ensured efficient operation.
• Continuous Improvement: Regular monitoring and feedback were essential for maintaining solution effectiveness.
• Training and Support: Providing comprehensive training ensured smooth adoption and effective use.
• Multilingual Content Management: Ensuring accurate translations and summaries across different languages required robust testing and adjustments.