# Attentional Extractive Summarization +
## 1 Introduction  
In recent years, automatic text summarization has made strides mainly due to two factors: the success of Deep Learning models and the use of a large amount of information available on the web for building large corpora in order to train the Deep Learning models. The automatic text summarization problem has been addressed in the literature using abstractive, extractive, or mixed approaches. Extractive approaches compose summaries by selecting sentences or words directly from the documents, whereas abstractive approaches build the summaries by paraphrasing/rewriting the sentences of the documents. Furthermore, there are also mixed strategies that combine extractive and abstractive techniques, performed in a decoupled way or simultaneously during the training of the models. Recently, an important effort has been made to develop abstractive methods. However, extractive approaches are still important in summarization, since they maintain the coherence, the factuality, and do not hallucinate like abstractive approaches do. Additionally, selecting sentences is also important for other tasks such as Text Classification, Question Answering and Information Extraction. Some successful approaches to extractive summarization are based on graph representations of the documents. This is the case with LexRank [1] and TextRank [2; 3; 4; 5], among others. Other approaches are based on Neural Networks. Typically, these neural network-based approaches have been addressed as a sequential binary sentence classification problem [6; 7; 8; 9; 10; 11; 12; 13; 14]. However, the available corpora do not directly provide this kindof labeling for training purposes since, in general, corpora only consist of (document, summary) pairs. In order to label the document sentences, prior to the training of the model, the most common strategy consists of using suboptimal extractive oracles [6; 7; 8; 9]. Additionally, several unsupervised approaches for extractive summarization have been proposed by [PERSON] et al. [15], and [PERSON] et al. [16]. Recently, Reinforcement Learning strategies have been extensively applied [10; 11; 12; 13] in order to dispense with the sentence labeling and optimizing directly the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metric [17]. The research objective of this work is to present in detail the general theoretical framework for extractive summarization called _Attentional Extractive Summarization_. Our proposal dispenses with the sentence labeling, avoiding the large computational cost required to compute near-optimal solutions, and allowing us to address the summarization problem in a simpler way than Reinforcement Learning techniques. Specifically, our proposal is based on the interpretation of the attention mechanisms of neural models that are trained to distinguish correct summaries for documents. It should be noted that it is only required a binary signal in order to train the model instead of sentence labeling. This training allows our system to learn relationships among document and summary sentences, and to replace the binary sequential sentence classification with a binary classification among documents and summaries. After the training of the model, it is possible to select the most attended sentences by focusing on the document sentence attentions computed by the model. The _Attentional Extractive Summarization_ framework was proposed with the aim of generalizing our previous proposals in extractive summarization and boosting future works and improvements under this framework. [PERSON] et al. [18] proposed a Siamese neural model based on Hierarchical Attention Networks [19]. Later, in [20], an extension to other types of attention mechanisms, in particular, to multi-head self-attention mechanisms of the Transformer Encoders [21], was proposed by [PERSON] et al.