# Abstractive vs. Extractive Summarization: An Experimental Review
## 2 Related Work
### Extractive Approaches  
The goal of extractive approaches is to extract the most important sentences of the document under consideration. These are assembled into a concise summary that captures the most significant aspects of the original text. Various algorithms have been proposed for extractive summarization, each utilizing different techniques for the sentence ranking and extraction step, including: (i) statistical ones, which utilize statistical metrics such as word or sentence frequency; (ii) graph-based ones, which model the document into a graph of sentences, and then utilize graph theory concepts (e.g., centrality, community detection measures, etc. ), and (iii) semantic-based ones, which model sentences and their terms into a co-occurrence matrix, which is then analyzed using distributional semantics [25]. In this context, this subsection discusses some of the most prominent approaches in extractive summarization, namely _Lulun, LSA, TextRank, LexRank, PositionRank_, and TopicRank_. _Lulun_[26] is one of the earliest approaches in extractive summarization. It utilizes statistical analysis to rank each sentence of a given text, based on the frequency of the most important words and their relative position in that sentence. The highest scoring sentences are extracted to form the final summary. However, this approach has a limitation, as it only focuses on individual words and does not consider the relationship between words or sentences. _Latent semantic analysis_ (_LSA_) was one of the earliest techniques used in an attempt to model the semantic relationships between words and capture key concepts in a document [27]. For the task of TS, the work of [28] proposed the _LSA_ technique, which models a document as a term-sentence matrix that represents the frequency of each word in each sentence of the document. Then, it applies _singular value decomposition_ (_SVD_) to extract the most important semantic features of the document in order to rank and extract the most important sentences. However, some drawbacks of this approach concern the dimensionality and the selection of sentences. To address them, [PERSON]. [29] built a _semantic_-based approach using _LSA_ that also used more advanced algorithms. Despite such improvements, summarization approaches built on _LSA_ can be computationally expensive, especially for larger texts, due to the use of _SVD_[30]. The use of graph-based algorithms is another extractive summarization approach that addresses some limitations of earlier approaches since it performs fast and scalable summarizations. One of the earliest and most prominent graph-based ranking approaches is _TextRank_[31]. The first step of this approach is the representation of the document as a weighted graph of sentences. The sentences of the document are represented as nodes and the relationships between them as edges. A connection between two sentences indicates that there is similarity between them, measured as a function of their overlapping content. After the graph is created, the _PageRank_ centrality algorithm [32] is applied to rank each sentence based on its connections to the other ones. Finally, the top-ranked sentences are selected to form a summary of the input document. The number of extracted sentences can be set as a user-defined parameter for the termination of the algorithm. _LexRank_[33] is another graph-based algorithm that relies on _PageRank_. Its key difference is that each sentence is represented as a vector of the _TF-IDF_ (_term frequency_--_inverse document frequency_) scores of the words it contains, while the relationship between these sentence vectors is measured using cosine similarity. A similarity matrix is created with each sentence represented as a row and column, and the elements of the matrix are computed as the cosine similarity score between the sentence vectors. Only similarities above a given threshold are included. To rank the sentences, _PageRank_ is applied. The number of selected sentences can be set similarly to _TextRank_.