Text Summarizer is a desktop application that leverages Natural Language Processing (NLP) techniques to automatically generate concise summaries of input text. The summarizer was implemented on a graphic user interface for easy usage.
- Text Input: Users can enter or paste a block of text into the input field of the graphical user interface.
- Text Processing: The application processes the text by tokenizing it into words and sentences. It removes common stopwords (e.g., "the," "and," "is") that do not contribute to the meaningful content of the text.
- Frequency Analysis: The application calculates the frequency of each significant word and scores sentences based on the sum of the frequencies of their constituent words.
- Summary Generation: Sentences are ranked according to their scores, and a summary is generated by selecting the most significant sentences that exceed a certain threshold relative to the average score of all sentences.
- Output: The summarized text is then displayed in the output area of the user interface, providing a quick overview of the original content.
This application is built using Python and PyQt5, incorporating NLP tools from the NLTK library to handle text processing and summarization tasks.
- Text Summarization: Efficiently extracts key sentences to create a concise summary.
- User-Friendly Interface: Simple and intuitive graphical user interface for text input and summary output.
- Stopword Removal: Filters out common stopwords to enhance the relevance of the summary.
- Python: Programming language used for application development.
- NLTK: Natural Language Toolkit for tokenization, stopword removal, and frequency analysis.
- PyQt5: Framework for building the graphical user interface.