Extractive Multi-document Summarization Using Multilayer Networks

TL;DR

This paper evaluates a multilayer network approach for extractive multi-document summarization, demonstrating that distinguishing between intra- and inter-layer connections improves summary quality.

Contribution

It introduces a multilayer network model that differentiates intra- and inter-document sentence connections, enhancing extractive summarization performance.

Findings

Discrimination between intra- and inter-layer edges improves summary relevance.

Multilayer network modeling enhances extractive multi-document summarization.

The approach offers a potential way to improve existing statistical text summarization methods.

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.