Extractive Text Summarization using BERT

Description:

The objective is to develop models capable of producing accurate and coherent summaries based on the article's title and text. Leveraging BERT, the models will learn intricate patterns and relationships within the training data, enabling them to automatically generate summaries that capture the main points and key information from the articles.The training data consists of a list of dictionaries in JSON format, where each element represents a distinct article with "title," "text," and "summary" keys.

The models will be fine-tuned using BERT's pre-trained weights and optimised through attention mechanisms, enabling them to effectively extract salient information for summary generation.

This project yields powerful tools for automatically generating extractive summaries for text articles. These models can be applied to various practical applications, including information retrieval systems, content summarization for news articles, blogs, or research papers, and aiding users in quickly comprehending lengthy texts. 

Tools & Technology

• Programming Language: Python

• Library: Summariser

Project Demo Video Implementation

We can develop projects with similar requirements tailored to your needs, or create custom solutions specific to your requirements. This project demo showcases the underlying code-level functionality, while your final product will be more accurate when trained on real data. Additionally, we offer UI interface development for both mobile and web platforms. Contact us today to launch your first Minimum Viable Product (MVP) in the field of AI and ML.