9.2 Extractive and abstractive summarization
6 min read•august 13, 2024
Text summarization comes in two flavors: extractive and abstractive. Extractive methods pick out key sentences, while abstractive ones generate new text. Each has its pros and cons, impacting how well they capture the original meaning.
Evaluating summaries is tricky. Automated metrics like ROUGE measure overlap with reference summaries, but they miss nuances. Human evaluation considers relevance, coherence, and factual accuracy, giving a more complete picture of summary quality.
Extractive vs Abstractive Summarization
Key Differences
Top images from around the web for Key Differences
The Stanford CoreNLP Natural Language Processing Toolkit - ACL Anthology View original
Is this image relevant?
Text Summarization View original
Is this image relevant?
On Extractive and Abstractive Neural Document Summarization with Transformer Language Models ... View original
Is this image relevant?
The Stanford CoreNLP Natural Language Processing Toolkit - ACL Anthology View original
Is this image relevant?
Text Summarization View original
Is this image relevant?
1 of 3
Top images from around the web for Key Differences
The Stanford CoreNLP Natural Language Processing Toolkit - ACL Anthology View original
Is this image relevant?
Text Summarization View original
Is this image relevant?
On Extractive and Abstractive Neural Document Summarization with Transformer Language Models ... View original
Is this image relevant?
The Stanford CoreNLP Natural Language Processing Toolkit - ACL Anthology View original
Is this image relevant?
Text Summarization View original
Is this image relevant?
1 of 3
- selects important sentences or phrases from the original text to create a summary, while generates new sentences that capture the meaning of the original text
- Extractive methods rely on statistical or machine learning techniques to identify key information (word frequency analysis, ), while abstractive methods use deep learning models to understand and paraphrase the content (, )
- Extractive summaries are more faithful to the original text but may lack coherence, while abstractive summaries can be more fluent and concise but may introduce inaccuracies or hallucinations
- Extractive summarization is generally simpler and faster to implement, while abstractive summarization requires more complex models and training data (large datasets of document-summary pairs)
Strengths and Weaknesses
- Extractive summarization preserves the original wording and guarantees faithfulness to the source text, but the selected sentences may not flow smoothly or capture the overall meaning
- Abstractive summarization can produce more human-like and coherent summaries by rephrasing and combining information, but it risks generating content that is not factually consistent with the original text
- Extractive methods are more scalable and require less computational resources, while abstractive methods need extensive training on large datasets and powerful hardware
- Extractive summaries are easier to interpret and trace back to the source, while abstractive summaries may be more engaging and informative for end-users
Extractive Summarization Techniques
Statistical and Machine Learning Approaches
- Statistical methods for extractive summarization include word frequency analysis, TF-IDF weighting, and graph-based ranking algorithms like and
- Word frequency analysis identifies the most common and representative words in the text
- TF-IDF weighs the importance of words based on their frequency in the document and rarity across the corpus
- Graph-based methods represent sentences as nodes and their similarity as edges, then rank the sentences based on their centrality or connectivity
- Machine learning approaches involve training classifiers or sequence labeling models to predict the importance or relevance of sentences based on features like position, length, and content
- Classifiers (Naive Bayes, SVM) can be trained on labeled data to categorize sentences as summary-worthy or not
- Sequence labeling models (CRF, HMM) can assign labels to each sentence indicating its role in the summary (introductory, conclusive, etc.)
- Unsupervised learning techniques like clustering and topic modeling can group similar sentences and identify representative ones for the summary
- Clustering algorithms (K-means, hierarchical) can partition sentences into coherent clusters and select the most central sentence from each cluster
- Topic models (LDA, NMF) can discover the latent themes in the text and choose sentences that best represent each topic
Evaluation Metrics
- Evaluation metrics for extractive summarization include ROUGE (Recall-Oriented Understudy for Gisting Evaluation), which measures overlap between the generated summary and reference summaries
- ROUGE-N computes the n-gram recall between the candidate and reference summaries
- ROUGE-L measures the longest common subsequence between the summaries, capturing fluency and word order
- ROUGE-SU4 counts skip-bigrams with a maximum gap of 4 words, allowing for non-consecutive matches
- Precision, recall, and F1 scores can be calculated based on the number of overlapping sentences or units between the generated and reference summaries
- Human evaluation can assess the quality of extractive summaries in terms of relevance, coverage, and readability
Neural Network Architectures for Summarization
Encoder-Decoder Models
- Abstractive summarization models are typically based on encoder-decoder architectures, where the encoder processes the input text and the decoder generates the summary
- The encoder (LSTM, GRU, Transformer) converts the input sequence into a fixed-length vector representation capturing its semantic content
- The decoder (LSTM, GRU, Transformer) takes the encoder output and generates the summary sequence word by word
- Attention mechanisms allow the decoder to focus on different parts of the input during generation, enabling better capturing of long-range dependencies and important information
- combine copying words from the source text with generating new words, allowing for more faithful summaries while maintaining abstractive capabilities
- A pointer network learns to select words from the input to copy into the summary
- A generator network produces a vocabulary distribution for generating new words
- A soft switch decides whether to copy or generate at each decoding step based on the context
Transformer-Based Models
- Transformer-based models like and have achieved state-of-the-art performance on abstractive summarization tasks by leveraging large-scale pretraining and fine-tuning
- BART (Bidirectional and Auto-Regressive Transformers) is pretrained as a denoising autoencoder, learning to reconstruct corrupted input sequences, and fine-tuned on summarization datasets
- T5 (Text-to-Text Transfer Transformer) is pretrained on a variety of NLP tasks framed as text-to-text problems, and fine-tuned on summarization by taking the input text and generating the summary
- Pretraining on large unlabeled corpora helps the models learn rich language representations and generalize better to downstream tasks
- Fine-tuning on summarization datasets adapts the models to the specific characteristics and requirements of the task
Training Techniques
- Training abstractive models requires large datasets of document-summary pairs, and techniques like teacher forcing and curriculum learning can be used to stabilize and improve the training process
- Teacher forcing provides the ground-truth summary as input to the decoder during training, helping it stay on track and learn faster
- Curriculum learning starts with easier examples (shorter texts, more extractive summaries) and gradually increases the difficulty, allowing the model to learn in a more structured way
- Regularization techniques like dropout, attention dropout, and label smoothing can prevent overfitting and improve generalization
- Beam search and length normalization can be used during inference to generate more diverse and balanced summaries
Evaluating Summary Quality
Automated Metrics
- Automated evaluation metrics for abstractive summarization include ROUGE, BLEU (Bilingual Evaluation Understudy), and , which measure lexical and between the generated summary and reference summaries
- ROUGE measures the overlap of n-grams, longest common subsequences, and skip-bigrams between the summaries
- BLEU calculates the precision of n-grams in the generated summary compared to the references, originally designed for machine translation but adapted for summarization
- BERTScore computes the cosine similarity between the contextualized word embeddings of the generated and reference summaries, capturing semantic equivalence beyond exact string matching
- These metrics provide a quick and scalable way to evaluate summary quality, but they have limitations in capturing higher-level aspects like coherence, relevance, and factual consistency
Human Evaluation
- Human evaluation involves having annotators rate the quality of summaries based on criteria like relevance, coherence, fluency, and factual consistency
- Relevance assesses how well the summary captures the main points and essential information from the source text
- Coherence measures the logical flow and structural integrity of the summary, ensuring that it is easy to follow and understand
- Fluency evaluates the linguistic quality and readability of the summary, checking for grammatical errors, awkward phrasing, and unnatural repetitions
- Factual consistency verifies that the summary does not contain any information that contradicts or is not supported by the source text
- Faithfulness and factuality are important aspects to evaluate in abstractive summaries, as models may generate content that is not supported by the original text
- Faithfulness can be assessed by checking whether each summary sentence can be traced back to a specific part of the source text
- Factuality can be tested by comparing the assertions made in the summary against external knowledge bases or fact-checking tools
- Comparative evaluation involves ranking or scoring multiple generated summaries against each other or against reference summaries
- Pairwise comparison asks annotators to choose the better summary between two options
- Likert scales allow annotators to rate each summary on a numerical scale (1-5) for different quality dimensions
- Qualitative analysis of generated summaries can provide insights into the strengths and weaknesses of different models and help identify areas for improvement
- Error analysis can reveal common patterns of mistakes, such as hallucinations, omissions, or redundancies
- Visualizing attention weights can show how the model is using the input information and what parts it is focusing on during generation
Key Terms to Review (23)
Abstractive summarization: Abstractive summarization is a technique in Natural Language Processing that involves generating a concise summary of a text by rephrasing and paraphrasing its content, rather than merely extracting sentences from the original text. This method aims to capture the main ideas and essential information while producing new sentences that may not directly reflect the source material. It contrasts with extractive summarization, which focuses on selecting and compiling existing sentences from the text.
Attention mechanisms: Attention mechanisms are computational techniques that help models focus on specific parts of input data while processing it, mimicking the way humans pay attention to certain information. By allowing models to weigh the importance of different input elements, attention mechanisms enhance performance in various tasks, enabling them to better capture context and relationships in sequential data, which is crucial for understanding and generating language.
BART: BART, which stands for Bidirectional and Auto-Regressive Transformers, is a model designed for both extractive and abstractive summarization tasks in natural language processing. It leverages a transformer architecture to read input text bidirectionally while generating summaries in an auto-regressive manner, making it effective for creating coherent and contextually relevant summaries from various types of text.
Bertscore: BERTScore is a metric used to evaluate the quality of generated text, particularly in natural language processing tasks like summarization, by leveraging contextual embeddings from the BERT model. It compares the similarity of candidate and reference texts by using cosine similarity between their word embeddings, providing a more nuanced understanding of semantic meaning rather than relying solely on exact matches.
BLEU Score: BLEU (Bilingual Evaluation Understudy) score is a metric used to evaluate the quality of text generated by machine translation systems by comparing it to one or more reference translations. This score measures how closely the generated output aligns with human translations, focusing on n-gram overlap to determine accuracy and fluency, making it a vital tool for assessing various applications in natural language processing.
Cnn/daily mail dataset: The CNN/Daily Mail dataset is a widely used benchmark dataset in natural language processing for evaluating text summarization algorithms. It consists of news articles and their corresponding summaries, making it an excellent resource for both extractive and abstractive summarization tasks, allowing researchers to assess how well models can generate concise summaries from longer texts.
Content summarization tools: Content summarization tools are software applications designed to automatically condense large volumes of text into shorter, coherent summaries, retaining the essential information and meaning. These tools utilize various techniques, including extractive and abstractive methods, to achieve their goal of simplifying content for easier understanding and analysis.
Encoder-decoder architectures: Encoder-decoder architectures are a type of neural network model designed to process input sequences and generate corresponding output sequences. This structure is especially useful in tasks like machine translation, where one sequence (like a sentence in English) is transformed into another sequence (like its translation in French). The encoder compresses the input into a context vector, while the decoder generates the output based on that vector, allowing the model to handle variable-length inputs and outputs effectively.
Extractive summarization: Extractive summarization is a technique in natural language processing that involves selecting and extracting key sentences or phrases from a text to create a concise summary. This method focuses on identifying the most important parts of the original document without altering the content, making it useful for quickly conveying essential information while preserving the original text's meaning and context.
Gigaword Dataset: The Gigaword Dataset is a large-scale collection of news articles that serves as a vital resource for training and evaluating natural language processing models, especially in summarization tasks. It contains millions of documents across various topics and has been widely used to develop algorithms for both extractive and abstractive summarization techniques, providing a comprehensive benchmark for researchers in the field.
Incoherence: Incoherence refers to a lack of logical connection or clarity in the presentation of information, making it difficult for the reader to understand the main ideas. In the context of summarization, incoherence can lead to a summary that fails to accurately represent the source material, causing confusion and misinterpretation of the original content.
Information Density: Information density refers to the amount of information conveyed in a given text or speech relative to its length. It plays a crucial role in determining how effectively content can be summarized, influencing both extractive and abstractive summarization techniques, which aim to condense information while preserving its meaning and context.
LexRank: LexRank is an algorithm used for extractive summarization that leverages the importance of sentences within a text by creating a graph representation of the sentences. The method evaluates the significance of each sentence based on its relationship to others, effectively identifying the most relevant sentences to include in a summary. This technique is particularly useful because it combines statistical approaches with graph theory to enhance the selection process for summarizing large volumes of information.
News aggregation: News aggregation is the process of collecting and compiling news stories and articles from various sources into a single platform or service. This allows users to access a wide range of information in one place, making it easier to stay informed about current events. News aggregation plays a crucial role in the dissemination of information, as it enables users to discover diverse perspectives and summaries from different outlets, often enhancing the understanding of complex topics.
Paraphrasing: Paraphrasing is the process of rephrasing or restating text or spoken language in one's own words while maintaining the original meaning. It is an essential skill that helps in clarifying concepts and enhancing understanding, allowing for the integration of information without directly copying the source. In various applications, paraphrasing can improve dialogue systems and support summarization techniques by distilling information into more concise or clearer expressions.
Pointer-generator networks: Pointer-generator networks are a type of neural network architecture that combines both extractive and abstractive summarization techniques. This model allows the generation of new words while also being able to copy words directly from the input text, making it highly effective for summarizing information. By utilizing a mechanism that decides whether to generate a word from the vocabulary or point to a word in the source text, it balances the strengths of both approaches in natural language processing tasks.
Redundancy: Redundancy refers to the unnecessary repetition of information within a text or dataset. In the context of summarization, it is crucial to minimize redundancy to produce concise and informative summaries that capture the essence of the original material without including repetitive details.
Rouge Score: The Rouge score is a set of metrics used to evaluate the quality of summaries by comparing them to reference summaries. It mainly focuses on recall, precision, and F1 score based on n-grams, which helps measure how much overlap there is between the generated and reference text. This evaluation method is particularly important for tasks like summarization, where assessing the relevance and informativeness of content is crucial.
Semantic similarity: Semantic similarity refers to the measure of how closely related or similar two pieces of text, such as words, sentences, or documents, are in terms of their meaning. This concept is central to understanding how language works, especially in computational linguistics, as it allows for the comparison of text based on context and meaning rather than just surface-level features like word choice. Understanding semantic similarity is crucial for tasks that involve natural language understanding, information retrieval, and summarization techniques.
Sentence selection: Sentence selection is the process of choosing specific sentences from a text to include in a summary or representation of that text. This method is essential in extractive summarization, where the goal is to create a condensed version of the original document while preserving its main ideas and important information.
T5: T5, or Text-To-Text Transfer Transformer, is a versatile model developed for natural language processing tasks that converts all language problems into a text-to-text format. This allows T5 to leverage the power of transformer architecture and attention mechanisms, enabling it to perform well in various tasks like summarization, translation, and question answering by treating them uniformly as text generation tasks.
Textrank: Textrank is an algorithm used for extractive summarization that ranks sentences in a document based on their importance and relevance to the overall context. By building a graph where sentences are nodes and edges represent similarities, Textrank identifies key sentences that can form a coherent summary. This method leverages the relationship between sentences, ensuring that the selected content reflects the document's main ideas without altering the original text.
Tf-idf: TF-IDF, or term frequency-inverse document frequency, is a statistical measure used to evaluate the importance of a word in a document relative to a collection of documents (or corpus). It highlights words that are more relevant to specific documents while reducing the weight of common words that appear frequently across all documents. This makes it an essential tool in various applications such as sentiment analysis, text indexing, retrieval models, question answering systems, text classification, and summarization.