What is Text Summarization
Text Summarization - Condensing documents into shorter representative text
A natural language processing task that generates concise summaries capturing the key information from longer documents. Text summarization creates compact representations for previewing and indexing content in multimodal search systems.
How It Works
Extractive summarization selects the most important sentences from the original document, while abstractive summarization generates new sentences that paraphrase the key points. Modern systems use transformer-based models that encode the full document and decode a summary. Large language models can produce high-quality abstractive summaries with appropriate prompting.
Technical Details
Extractive methods use sentence scoring based on position, term frequency, and graph-based centrality (TextRank). Abstractive models like BART, PEGASUS, and T5 are encoder-decoder transformers fine-tuned on summarization datasets. LLM-based summarization uses prompting or fine-tuning for controllable summary generation. Evaluation metrics include ROUGE (n-gram overlap), BERTScore (semantic similarity), and human preference ratings.
Best Practices
- Use abstractive summarization for natural-sounding summaries and extractive for faithful extraction
- Specify desired summary length and focus areas in prompts for LLM-based summarization
- Generate summaries of different lengths for different use cases (preview, full summary, bullet points)
- Store summaries alongside full documents to enable both quick scanning and detailed retrieval
Common Pitfalls
- Not evaluating for factual consistency, as abstractive models can introduce hallucinated facts
- Using ROUGE scores as the sole quality metric without human evaluation
- Summarizing very long documents without chunking, causing important information to be truncated
- Generating summaries that lose critical details needed for accurate search and retrieval
Advanced Tips
- Use summarization to create text descriptions of non-text content (video summaries, chart descriptions)
- Implement multi-document summarization to synthesize information across related documents
- Apply query-focused summarization that generates summaries relevant to specific user queries
- Combine extractive and abstractive approaches for summaries that are both faithful and fluent