# Extractive Multi-document Summarization Using Multilayer Networks

arXiv: https://arxiv.org/abs/1711.02608 · HTML: https://mlsanity.com/paper/1711.02608/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2017.

## Built on by (most cited first, 5 of 5)

- [DESCGEN: A Distantly Supervised Dataset for Generating Abstractive Entity Descriptions](https://mlsanity.com/paper/2106.05365/) (2021)
- [EventSum: A Large-Scale Event-Centric Summarization Dataset for Chinese Multi-News Documents](https://mlsanity.com/paper/2412.11814/) (2024)
- [Quantitative Discourse Cohesion Analysis of Scientific Scholarly Texts using Multilayer Networks](https://mlsanity.com/paper/2205.07532/) (2022)
- [On predicting research grants productivity](https://mlsanity.com/paper/2106.10700/) (2021)
- [Using virtual edges to extract keywords from texts modeled as complex networks](https://mlsanity.com/paper/2205.02172/) (2022)

## Abstract (arXiv)

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.