Start writing here...
Here’s a clear and well-structured overview of Text Summarization — great for learning, note-taking, or presentations:
📝 Text Summarization in NLP
📌 What is Text Summarization?
Text Summarization is the process of automatically shortening a text document to produce a concise and meaningful version that preserves key information and intent.
🧠 Why Is It Important?
- Saves time by condensing long documents
- Enhances information retrieval
- Useful in news, legal docs, research papers, customer support, etc.
🔍 Types of Text Summarization
| Type | Description | Example |
|---|---|---|
| Extractive | Selects key sentences or phrases from the original text | Like highlighting |
| Abstractive | Generates new sentences that capture the core idea, like human-written summaries | Like paraphrasing |
🛠️ Techniques for Summarization
🔹 Extractive Methods:
- TF-IDF: Scores sentences based on word importance
- TextRank: Graph-based ranking (like PageRank for sentences)
- LexRank, SumBasic, etc.
🔹 Abstractive Methods:
- Use sequence-to-sequence (Seq2Seq) models or Transformer models
-
Examples:
- BART
- T5
- PEGASUS
- GPT-based summarization
🧪 Example
Original Text:
"The Eiffel Tower is one of the most iconic landmarks in the world. Located in Paris, France, it was completed in 1889 and attracts millions of tourists each year."
-
Extractive Summary:
→ "The Eiffel Tower is one of the most iconic landmarks in the world." -
Abstractive Summary:
→ "Paris’s Eiffel Tower, built in 1889, is a top tourist attraction."
⚙️ Tools & Libraries
- Hugging Face Transformers (BART, T5, GPT, etc.)
- spaCy (basic extractive)
- Sumy, Gensim (for extractive)
- OpenAI API / ChatGPT (for abstractive summaries)
🚧 Challenges
- Faithfulness: Abstractive summaries may generate false information
- Coherence: Making sure the summary reads well
- Evaluation: Metrics like ROUGE, BLEU, and human judgment used
Would you like a code example (e.g., using Hugging Face’s BART or T5) or a comparison between two summarization outputs?