Abstract Huge volumes of textual information has been produced every single day. In order to organize and understand such large datasets, in recent years, summarization techniques have become popular. These techniques aims at finding relevant, concise and non-redundant content from such a big data. While network methods have been adopted to model texts in some scenarios, a systematic evaluation of multilayer network models in the multi-document summarization task has been limited to a few studies. Here, we evaluate the performance of a multilayer-based method to select the most relevant sentences in the context of an extractive multi document summarization (MDS) task. In the adopted model, nodes represent sentences and edges are created based on the number of shared words between sentences. Differently from previous studies in multi-document summarization, we make a distinction between edges linking sentences from different documents (inter-layer) and those connecting sentences from the same document (intra-layer). As a proof of principle, our results reveal that such a discrimination between intra- and inter-layer in a multilayered representation is able to improve the quality of the generated summaries. This piece of information could be used to improve current statistical methods and related textual models.
Several works have addressed the task of extractive summarization based on complex networks tools and methods. For example, in the work of @cite_22 , the MDS task was applied for documents in Brazilian Portuguese. The authors first extracted all sentences from the cluster of documents and then they modelled them as a single network. After the pre-processing stage, sentences were represented as nodes, which were linked by traditional similarity measurements. In order to select the best ranked sentences, the authors used simple network measurements, including degree, clustering coefficient and average shortest path length. A simple heuristic to avoid redundant sentences in the generated summaries was also applied. The results showed that the proposed method yielded competitive results, which were close to the best statistical systems available for the Portuguese language. Even though this work addressed the MDS with a graph-based approach, no distinction between intra- and inter-layer edges was considered.
@cite_22
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In this work we investigate the use of graphs for multi-document summarization. We adapt the traditional Relationship Map approach to the multi-document scenario and, in a hybrid approach, we consider adding CST (Cross-document Structure Theory) relations to this adapted model. We also investigate some measures derived from graphs and complex networks for sentence selection. We show that the superficial graph-based methods are promising for the task. More importantly, some of them perform almost as good as a deep approach.