Introduction
Poems often contain deep meanings, emotions and creative expressions using a limited number of words. Understanding the complete meaning of a poem can sometimes be challenging for readers.
The Poem Summarization Using Deep Learning project uses Artificial Intelligence and Natural Language Processing (NLP) to generate shorter versions of poems while preserving their important themes and meanings.
The system analyzes the poem, identifies meaningful words and sentences, and generates a concise summary using deep learning techniques. This project demonstrates how AI can support automated text understanding and summarization.
Key Features & Benefits
Working Methodology
The proposed system follows these steps:
1. Text Input: Collect the poem from the user.
2. Text Preprocessing: Remove unnecessary characters and normalize the text.
3. Tokenization: Divide the poem into meaningful words and sentences.
4. Feature Extraction: Identify important linguistic patterns.
5. Deep Learning: Process the poem using a trained NLP model.
6. Summary Generation: Generate a shorter version while preserving the main ideas.
7. Result Display: Display the generated summary to the user.
Models such as LSTM, Seq2Seq and Transformer-based architectures can be explored for poem summarization.
Implementation Guide
Key Features
- Automatic poem summarization.
- Natural language processing.
- Text preprocessing and tokenization.
- Deep learning-based text analysis.
- Important theme identification.
- Short and readable summary generation.
- User-friendly interface.
- Poetry analysis and education.
- Digital libraries.
- Automated literary content processing.
- Educational text summarization.
- AI-based language applications.
Technical Specifications
Implementation Steps
1. Collect a suitable poem dataset.
2. Clean and preprocess the poem text.
3. Tokenize the poems into words and sentences.
4. Prepare training and testing datasets.
5. Build a deep learning summarization model.
6. Train and evaluate the model.
7. Develop a web application using Flask.
8. Display the generated poem summary.
Advantages
- Reduces lengthy poems into shorter summaries.
- Automates text analysis.
- Helps readers understand important themes.
- Demonstrates practical NLP applications.
- Supports deep learning experimentation.
Future Scope
The project can be enhanced using advanced Transformer models such as T5 and BART. Future versions can include multilingual poem summarization, emotion detection, theme extraction and interactive literary analysis.
Conclusion & Next Steps
Conclusion
The Poem Summarization Using Deep Learning project demonstrates how artificial intelligence can simplify literary text analysis. By combining NLP, text preprocessing and deep learning techniques, the system generates concise summaries of poems.
This project helps students understand natural language processing, neural networks, text generation and deep learning applications. It can be further improved using Transformer-based language models and multilingual support.
Aislyn Technologies – Bangalore
Contact Details
Phone: +91 97395 94609
Email: info@aislyntech.com
Website: https://aislyn.in
Contact us today for expert guidance and support with your BCA, Python, AI and Deep Learning projects.
