Automatic Review Summarization
NLP · Natural Language Processing
This project involved the development of a system capable of generating automatic summaries from user-written reviews, applying natural language processing techniques. The goal was to enable faster understanding of long opinions about products or services.
Objective
To design a model that takes multiple reviews as input and produces a coherent and concise summary, highlighting the most relevant aspects mentioned by users.
Tools and Technologies
- Python (pandas, nltk, spaCy, scikit-learn)
- TF-IDF for feature extraction
- Extractive and compressive summarization algorithms
- Evaluation with ROUGE metrics and qualitative analysis
- Interactive testing interface in Jupyter Notebook
Results
The system was able to generate relevant summaries from sets of reviews, achieving good coverage of the key points expressed by users. It improved content comprehension by reducing the text volume by up to 70% without losing essential information.
Key Learnings
This project strengthened my knowledge in NLP, especially in automatic summarization techniques and text analysis. I also improved my skills in evaluating models with NLP-specific metrics and designing interpretable solutions for end users.