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.
Understanding Abstractive Summarization
Abstractive summarization goes beyond simply extracting sentences from a source document. Instead, it generates entirely new phrases and sentences that capture the core meaning of the original text, much like a human would write a summary from memory. This approach relies on advanced natural language generation techniques, often powered by large language models and transformer architectures such as BART and T5. Unlike extractive summarization, which copies verbatim passages, abstractive methods can paraphrase, merge ideas, and produce more fluent and concise outputs. Real-world applications include news headline generation, meeting note condensation, and medical report summarization. The main challenges involve maintaining factual accuracy and avoiding hallucination, making ground truth evaluation and human-in-the-loop validation critical components of any production pipeline.
Category
Natural Language Processing
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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.
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.
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.