Extractive and Abstractive Text Summarization

In the field of Natural Language Processing (NLP), text summarization plays a crucial role in condensing lengthy documents or articles into concise and informative summaries. Extractive and abstractive text summarization are two popular approaches used to automatically generate summaries. In this article, we will explore the differences between these two techniques and their applications.

Extractive Text Summarization

Extractive summarization involves selecting the most important sentences or phrases from the original text and combining them to form a summary. This technique extracts information directly from the source text and does not modify or paraphrase the text. The extracted sentences are chosen based on various features such as word frequency, importance of sentences, or similarity to the main theme of the document.

The key advantage of extractive summarization is that it maintains the original context and wording of the sentences. This approach also requires less computational power compared to abstractive summarization. However, it may struggle with generating coherent summaries, as the chosen sentences might not always flow smoothly when combined.

Extractive text summarization can be implemented using various algorithms, such as graph-based models, clustering techniques, or machine learning algorithms. One common method is the TextRank algorithm, which applies a graph-based ranking algorithm to identify the most important sentences based on the similarity between sentences.

This technique has found applications in various domains, including news article summarization, review summarization, and document summarization. It is particularly useful when the main objective is to present the most relevant and essential information in a concise manner without introducing any new information.

Abstractive Text Summarization

Abstractive summarization, on the other hand, aims to generate a summary that captures the essence of the original text but may use different wording and sentence structures. This technique involves understanding the source text, interpreting its meaning, and generating new sentences that effectively convey the same information.

Unlike extractive summarization, abstractive summarization requires higher levels of natural language understanding and generation. It relies on techniques such as natural language processing, machine learning, and deep learning models to generate coherent summaries. Advanced models like transformers that employ techniques like sequence-to-sequence or reinforcement learning are often used for abstractive summarization tasks.

The advantage of abstractive summarization is the ability to generate more human-like summaries that can overcome the limitations of extractive methods. However, it can be more challenging to implement and may introduce semantic errors or generate incorrect information due to the nature of generating new sentences.

Abstractive text summarization is widely used in applications such as social media summarization, book summarization, and generating summaries for conversations or dialogues. It is particularly beneficial when the goal is to provide concise and informative summaries that capture the key points of the original text while eliminating redundant or irrelevant information.


Both extractive and abstractive text summarization techniques have their own strengths and applications. Extractive summarization maintains the original context but may struggle with generating coherent summaries, while abstractive summarization generates more human-like summaries but can introduce semantic errors. The choice between these approaches depends on the specific requirements and constraints of the summarization task. Researchers and practitioners continue to explore and develop new techniques to improve the quality and efficiency of text summarization, making it an exciting field in NLP.

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