# Summarization of Spanish Talk Shows with Siamese Hierarchical Attention Networks
## 1 Introduction  
Nowadays, the development of automatic summarization systems is an important issue due to the great amount of information in different formats that is accessible in the web or in other repositories. Some summarization systems are based on unsupervised learning approaches by considering statistical features of words [1], topic modeling such as Latent Semantic Analysis [2], graph based approaches such as LexRank [3, 4] (for a more exhaustive review see [5, 6]). There are also systems based on supervised learning techniques such as Conditional Random Fields [7], Support Vector Machines [8] and Neural Networks [9, 10, 11, 12, 13, 14, 15]. Although most of the works are focused on the application of automatic summarization techniques to collections of purely textual documents, summarization systems are not limited to text input tasks. There are some other works that address the problem of adapting these techniques to audio recordings as input, typically broadcast news, lectures or meetings [16, 17, 18]. These systems have to tackle with specific problems derived from the errors generated by the speech recognition phase such as misrecognized words and errors in punctuation marks. Just as the volume of textual documents available on the web has grown dramatically in recent years, the same is true in the case of TV programs collections. The television channels make available to the public the programs of their own production, generating with it large collections of videos. For the audience who could not follow the broadcast of the live programs, it is interesting the possibility of accessing them. Therefore, in addition to an adequate information retrieval system to perform the search in the programs collections, an automatic summarization system applied to these programswill be helpful for searching information. This is especially interesting for talk shows, which consist of several speakers giving opinions on various topics introduced by the program's presenter. The application of supervised methods to automatic summarization, as those based on Neural Networks, implies the availability of adequate corpora consisting of a set of document-summary pairs. The construction of large and high quality corpora for this purpose is not an easy task, because it is necessary a great human effort to generate thousands of manual summaries, or to design new approaches to obtain these summaries in a semiautomatic way. The first important resource for learning corpus-based summarization models is the CNN/DailyMail summarization corpus, originally constructed by [PERSON] [19] for the task of passage-based question answering and adapted to the document summarization task [9; 10]. It consists of news stories from CNN and DailyMail and contains 312,077 document-summary pairs. It have been developed appropriate corpora for English, however this is not the same for other languages, such as Spanish. With the aim of building a corpus for Spanish, a strategy similar to the proposed in Reference [20] for the construction of the NEWSROOM corpus has been followed in this work. In Reference [20], they take advantage of the highlights or summaries, provided by authors or editors in the newsroom, in order to obtain the summaries. A crawling process on the newspaper websites extracts articles and summaries in a straightforward way. The NEWSROOM corpus contains news, sports, entertainment, financial and other kind of publications from several English newspapers. In this work, we have built a corpus of Spanish newspapers--the ES-NEWS corpus. It consists of a set of 277,675 article-summary pairs extracted from 11 different Spanish newspapers. ES-NEWS corpus contains articles and summaries of news, sports, politics, culture and other topics. The use of this corpus in this work is two-fold--on the one hand, we evaluate our summarization system [15] with it in order to study the transferability of our system from English to Spanish and on the other hand, we use it to train the system that we apply to the summarization of Spanish talk shows.