# Abstractive vs. Extractive Summarization: An Experimental Review
###### Abstract  
Text summarization is a subtask of natural language processing referring to the automatic creation of a concise and fluent summary that captures the main ideas and topics from one or multiple documents. Earlier literature surveys focus on extractive approaches, which rank the _top-n_ most important sentences in the input document and then combine them to form a summary. As argued in the literature, the summaries of these approaches do not have the same lexical flow or coherence as summaries that are manually produced by humans. Newer surveys elaborate abstractive approaches, which generate a summary with potentially new phrases and sentences compared to the input document. Generally speaking, contrary to the extractive approaches, the abstractive ones create summaries that are more similar to those produced by humans. However, these approaches still lack the contextual representation needed to form fluent summaries. Recent advancements in deep learning and pretrained language models led to the improvement of many natural language processing tasks, including abstractive summarization. Overall, these surveys do not present a comprehensive evaluation framework that assesses the aforementioned approaches. Taking the above into account, the contribution of this survey is fourfold: (i) we provide a comprehensive survey of the state-of-the-art approaches in text summarization; (ii) we conduct a comparative evaluation of these approaches, using well-known datasets from the related literature, as well as popular evaluation scores such as _KOUGE-1_, _ROUGE-2_, _ROUGE-L_, _ROUGE-LSUM_, _BLEU-1_, _BLEU-2_ and _SACREBLEU_; (iii) we report on insights gained on various aspects of the text summarization process,  
Diverse TS applications already exist, including: (i) literature summarization, aiming to handle a long document such as a book, a scientific article, or similar literature resources [5; 6]; (ii) news outlet summarization, aiming to summarize information from one or multiple news outlet portals [7; 8]; (iii) e-mail summarization [9; 10]; (iv) legal document summarization, focusing on the extraction of important aspects from lengthy legal documents [11; 12; 13]; (v) social media summarization, where social media posts from multiple users are summarized in order to measure the social impact of a certain topic (this application is highly relevant to the field of opinion mining) [14; 15]; (vi) argument summarization, as a means for meaningfully aggregating the public opinion in a digital democracy platform [16]. Recently published works outlined a series of insights about diverse TS characteristics. For instance, Ref. [17] assessed various techniques used by extractive summarization approaches, as well as the associated evaluation metrics; Ref. [18] presented an overview of TS datasets, approaches, and evaluation schemas; Ref. [4] elaborated on a comprehensive classification schema of TS approaches, based on their underlying techniques, and performed a comparative assessment of their performance through the use of various metrics; Ref. [19] focused on extractive TS approaches, evaluation metrics, and their limitations; Ref. [3] offered a comprehensive survey of TS and keyword extraction, the latter being a sibling task of TS. In addition, Refs. [2; 20] are two comprehensive surveys of TS applications, approaches, datasets, and evaluation techniques, also reporting on associated limitations and challenges. Contrary to earlier works, [PERSON]. [21] reported a comprehensive survey that focused only on abstractive TS approaches, taking into account recent deep learning approaches, while also presenting their comparative evaluation using various versions of the _ROUGE_ metric [22].