Applying Siamese Hierarchical Attention Neural Networks for multi-document summarization

José Ángel González, Julien Delonca, Emilio Sanchis, Fernando García-Granada, Encarna Segarra


In this paper, we present an approach to multi-document summarization based on Siamese Hierarchical Attention Neural Networks. The attention mechanism of Hierarchical Attention Networks, provides a score to each sentence in function of its relevance in the classification process. For the summarization process, only the scores of sentences are used to rank them and select the most salient sentences. In this work we explore the adaptability of this model to the problem of multidocument summarization (typically very long documents where the straightforward application of neural networks tends to fail). The experiments were carried out using the CNN/DailyMail as training corpus, and the DUC-2007 as test corpus. Despite the difference between training set (CNN/DailyMail) and test set (DUC-2007) characteristics, the results show the adequacy of this approach to multi-document summarization.

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