The problem Notimiser solves

Notimiser solves the problem of information overload and time-consuming text analysis. It streamlines the process of summarizing academic and non-academic texts, making it easier for users to digest and comprehend large volumes of information efficiently. By automating the summarization process using machine learning techniques, Notimiser eliminates the need for manual extraction of key points and condenses lengthy texts into concise summaries. This saves users valuable time, improves productivity, and enhances the overall learning experience. Additionally, Notimiser's ability to handle image and PDF uploads further simplifies the task of analyzing and summarizing various types of content.

Challenges I ran into

During the development of Notimiser, one of the challenges we encountered was ensuring the accuracy and coherence of the generated summaries. Due to the complexity and diversity of academic and non-academic texts, it was difficult to create a single summarization model that consistently produced high-quality results across all types of content.

To overcome this challenge, we employed a combination of pre-training and fine-tuning techniques. We initially trained our model on a large corpus of diverse texts to learn general language patterns. Then, we fine-tuned the model using specific datasets that were carefully curated to represent different domains and writing styles. This approach helped us improve the accuracy and relevance of the generated summaries.