Turkish Text Summarizer
Full-stack project that uses NLP techniques, including cosine similarity and PageRank, to generate concise summaries of Turkish texts. It features a scalable back-end for efficient processing and an interactive front-end for user-friendly input and visualization, delivering accurate and intuitive summarization results.
I developed a full-stack solution aimed at automating the summarization of Turkish text using Natural Language Processing (NLP) techniques.
The core of the project was the NLP algorithm, where I created a Turkish text summarization model that utilized cosine similarity and PageRank for ranking and extracting the most relevant
insights from the text. This approach allowed for a more accurate and meaningful summarization process, tailored specifically for Turkish language content.
On the backend, I focused on optimizing the text processing pipeline to ensure scalability and accuracy for large text inputs. I integrated machine learning models to refine the summarization,
allowing for dynamic adjustments based on text complexity. On the frontend, I built a responsive web interface that allowed users to input text, view summaries, and interact with visualizations of the summarized content.
The project was built as a full-stack application, connecting the backend processing with the frontend interface seamlessly, ensuring a smooth and intuitive user experience. By focusing on user-centric design,
I ensured the system was easy to navigate, making it accessible for users with varying technical expertise. This project not only honed my skills in NLP and full-stack development but also deepened my understanding of how to create useful,
real-world applications with advanced text-processing techniques.