This was the Minor Project for my 4th Semester of Engineering.
Text summarization is a vital aspect of Natural Language Processing (NLP), particularly in the domain of car/product reviews, where extracting concise insights from a plethora of textual data is crucial. Our project aims to revolutionize text summarization by incorporating advanced NLP techniques and domain-specific features tailored for analyzing car reviews. With an increasing volume of reviews available online, manually summarizing this vast amount of information becomes challenging. Our system addresses this challenge by leveraging NLP methods to automatically extract key insights, including sentiment analysis, frequency count, part-of-speech tagging, and generating visualizations such as knowledge graphs and dependency graphs. Through our comprehensive approach, we aim to enhance the accuracy, relevance, and usability of text summarization systems in the domain of car reviews, offering users a rich and informative summary of their desired content.
- A user-friendly website interface that seamlessly integrates both general text summarization functionality and specialized car review summarization features.
- Generate insightful visualizations such as knowledge graphs and dependency graphs to illustrate semantic relationships, dependencies, and key insights within the text summaries
- Generate accurate summaries when faced with noisy or poorly structured input documents
- Clone the repository:
https://github.com/adarsh-naik-2004/NLP-Based-Text-Summarizer.git- Run the app.py file If any library is not installed , then install it by running this code in the terminal.
pip install library_name
