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Extractive multi-document summarization using multilayer networks

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  • Tohalino, Jorge V.
  • Amancio, Diego R.

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.

Suggested Citation

  • Tohalino, Jorge V. & Amancio, Diego R., 2018. "Extractive multi-document summarization using multilayer networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 526-539.
    • Handle: RePEc:eee:phsmap:v:503:y:2018:i:c:p:526-539
    DOI: 10.1016/j.physa.2018.03.013
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    References listed on IDEAS

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    1. Amancio, Diego R. & Nunes, Maria G.V. & Oliveira, Osvaldo N. & Costa, Luciano da F., 2012. "Extractive summarization using complex networks and syntactic dependency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1855-1864.
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    5. Diego Raphael Amancio, 2015. "Comparing the topological properties of real and artificially generated scientific manuscripts," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1763-1779, December.
    6. Silva, Filipi N. & Amancio, Diego R. & Bardosova, Maria & Costa, Luciano da F. & Oliveira, Osvaldo N., 2016. "Using network science and text analytics to produce surveys in a scientific topic," Journal of Informetrics, Elsevier, vol. 10(2), pages 487-502.
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    8. Diego Raphael Amancio, 2015. "A Complex Network Approach to Stylometry," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-21, August.
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    Citations

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    Cited by:

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    2. Brito, Ana C.M. & Silva, Filipi N. & Amancio, Diego R., 2021. "Associations between author-level metrics in subsequent time periods," Journal of Informetrics, Elsevier, vol. 15(4).
    3. Corrêa, Edilson A. & Marinho, Vanessa Q. & Amancio, Diego R., 2020. "Semantic flow in language networks discriminates texts by genre and publication date," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    4. Liu, Yanyan & Li, Keping & Yan, Dongyang & Gu, Shuang, 2022. "A network-based CNN model to identify the hidden information in text data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).
    5. Ana C. M. Brito & Filipi N. Silva & Diego R. Amancio, 2023. "Analyzing the influence of prolific collaborations on authors productivity and visibility," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(4), pages 2471-2487, April.
    6. Xu, Haiyun & Yue, Zenghui & Pang, Hongshen & Elahi, Ehsan & Li, Jing & Wang, Lu, 2022. "Integrative model for discovering linked topics in science and technology," Journal of Informetrics, Elsevier, vol. 16(2).
    7. Tohalino, Jorge A.V. & Amancio, Diego R., 2022. "On predicting research grants productivity via machine learning," Journal of Informetrics, Elsevier, vol. 16(2).

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