# Summarization of Spanish Talk Shows with Siamese Hierarchical Attention Networks
## 4 Related Work  
Many Neural Network based approaches for text summarization have been proposed in the last few years. Most of them are based on encoder-decoder architectures. Generally, in these approaches, the encoder reads the source sequence as a list of continuous-space representations from which the decoder generates the target sequence. Some of these approaches also incorporate attention mechanisms. In particular, [PERSON] [9] proposed an attentional encoder-decoder approach for extractive single-document summarization and [PERSON] [11] presented an extractive summarization approach based on sentence classification using Neural Networks and required a previous adaptation of the corpus based on ROUGE. In both works, they used the CNN/DailyMail corpus since its large size makes it attractive for training deep Neural Networks. Other Neural Network based works try to improve the behavior of the models incorporating more information. This is the case of [PERSON] [24] where they jointly learn the attention mechanism, to obtain the score of the sentences and the selection mechanism to extract the most salient sentences. One recent trend consists in addressing the summarization problem as a sentence ranking task by considering Reinforcement Learning. This is typically done with the aim of optimizing discrete metrics that are not differentiable, such as the ROUGE evaluation metric [25; 26].  
There have been some attempts to address the problem of summarization of speech. Most of them are based on techniques successfully used for text summarization and they are directly applied to the output of the speech recognition process. Reference [16] presented an approach to this problem where salient sentences or segments, are extracted only using the textual information, by means of concatenation and reordering mechanisms the final summaries are generated. Experiments are performed on monologues such as lectures, presentations and news commentaries. In Reference [17], an extractive summarization approach also based on the textual representation of the audio is also presented. Salient sentences are extracted based on Language Model measures. Experiments are performed on the Mandarin broadcast news corpus MATBN [27] which was manually segmented and transcribed for evaluation purposes. It must be noted that this kind of corpus has a more regular structure than other speech programs as interviews or debates. Reference [18] also addresses the speech summarization problem but in this case, by using Convolutional Neural Networks for sentence selection. The system includes two convolutional networks, one of them working on the document and the other one working on a sentence of the same document. The system learns for each document sentence a score that represents its probability of belonging to the summary. They also use the MATBN corpus for evaluation purposes.  
The SHA-NN system [15] is based on addressing a binary classification problem in order to select the most relevant sentences by means of the attention mechanisms. This system, differently from some Neural Networks based mentioned works, does not require the preparation of the corpus [11], being the system which learns the alignment between document and summary. Moreover, our system addresses the problem as a binary classification task in order to distinguish correct summaries for documents, instead of performing sentence classification to score the document sentences [9; 11; 18].