Problem

Language translation and summarization are crucial tasks in natural language processing, but the existing methods often fail to generate accurate results due to the complexity of human language. Our team faced a similar challenge while working on a project that required us to develop an algorithm for precise keyword extraction and text summarization.

We realized that traditional machine learning techniques were not sufficient for this task, as they could not capture the nuances of human language. Therefore, we needed to explore advanced algorithms like LSTM (Long Short-Term Memory) to improve the accuracy of our model.

Learn more about LSTM here.

Solution

To overcome the limitations of traditional machine learning algorithms, we implemented an LSTM-based model for keyword extraction and text summarization. This approach allowed us to capture long-term dependencies in the input data and generate more accurate results.

We also took a proactive approach to data preparation by curating a dataset of 3000 instances for robust summarization. This helped us train our model effectively and improve its performance over time.

Impact

Achieved a BLEU score of 0.7, indicating the effectiveness of our LSTM-based model in generating precise translations and summaries
Improved accuracy and efficiency in language processing tasks, leading to faster turnaround times for translation projects

"This project has revolutionized our approach to language processing tasks and has significantly improved our workflow."

Team

Kalyani Laddha - Batch Mate
- Implemented the LSTM-based model for keyword extraction and text summarization
- Analyzed the results and fine-tuned the model to improve its accuracy over time
Akash Shekhavat - Batch Mate
- Curated a dataset of 3000 instances for robust summarization
- Preprocessed the data and prepared it for training the LSTM-based model

LSTM Model Boosts Translation Accuracy