Problem

Our client, a news organization, was struggling to keep up with the demand for timely and accurate summaries of news articles. Their current process involved manually summarizing each article, which was time-consuming and often resulted in errors or inconsistencies. They needed a more efficient and reliable solution.

After researching various options, we determined that abstractive text summarization using deep learning models would be the most effective approach. However, this technology was still relatively new and untested in real-world applications.

Learn more about our approach here.

Solution

We developed a custom solution using a combination of RNN, LSTM, NLTK, seq2seq model, and NLP technologies to create an abstractive text summarization tool. Our team trained the model on a large dataset of news articles to ensure accuracy and reliability.

The tool we created is able to analyze an entire article and generate a concise summary that captures the most important information. This process is fully automated, saving our client time and resources while also improving the quality of their summaries.

Impact

Increased efficiency by X%
Improved accuracy of summaries by X%
Reduced workload for employees by X hours per week

"This tool has revolutionized our workflow and allowed us to provide better service to our readers." - CEO of News Organization

Team

Team Member #1 - Data Scientist
- Developed and trained the deep learning model
- Optimized the model for accuracy and efficiency
Team Member #2 - Software Engineer
- Built the tool's user interface and integration with client's systems
- Oversaw testing and implementation of the tool in production environment

News Org Automates Summaries with AI Tool