Extractive Summarization
Extractive summarization selects and combines the most important sentences directly from a source document to create a summary. It preserves the original wording but may lack the coherence of abstractive approaches.
Understanding Extractive Summarization
Extractive summarization is a text summarization approach that works by identifying and selecting the most important sentences or passages directly from the source document, assembling them into a shorter summary without generating new text. This method preserves the original wording and is generally more faithful to the source material compared to abstractive summarization, though it can produce less fluent or cohesive results. Common techniques include scoring sentences based on term frequency, information gain, or neural network-based relevance models. Extractive summarization is widely used in news aggregation, legal document review, and search engine snippet generation where factual accuracy is paramount. Modern systems often combine extractive methods with transformer-based models to improve sentence selection, and some pipelines use extractive approaches as a first stage before applying abstractive refinement.
Category
Natural Language Processing
Is AI recommending your brand?
Find out if ChatGPT, Perplexity, and Gemini mention you when people search your industry.
Check your brand — $9Related Natural Language Processing Terms
Abstractive Summarization
Abstractive summarization generates new text that captures the key points of a longer document, rather than simply extracting existing sentences. It requires deep language understanding and generation capabilities.
Beam Search
Beam search is a decoding algorithm that explores multiple candidate sequences simultaneously, keeping only the top-k most promising at each step. It balances between greedy decoding and exhaustive search in text generation.
BERT
BERT (Bidirectional Encoder Representations from Transformers) is a language model developed by Google that reads text in both directions simultaneously. BERT revolutionized NLP by enabling deep bidirectional pre-training for language understanding tasks.
Bigram
A bigram is a contiguous sequence of two items (typically words or characters) from a given text. Bigram models estimate the probability of a word based on the immediately preceding word.
Byte Pair Encoding
Byte Pair Encoding (BPE) is a subword tokenization algorithm that iteratively merges the most frequent pairs of characters or character sequences. BPE is widely used in modern language models to handle rare words and multilingual text.
Corpus
A corpus is a large, structured collection of text documents used for training and evaluating natural language processing models. The quality and diversity of a training corpus significantly impacts model performance.
Grounding
Grounding in AI refers to connecting a model's language understanding to real-world knowledge, data, or sensory experience. Grounded AI systems produce more factual and contextually relevant outputs.
Language Model
A language model is an AI system that learns the probability distribution of sequences of words in a language. Modern language models like GPT and Claude can generate text, answer questions, and perform complex reasoning.