IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v126y2021i1d10.1007_s11192-020-03732-x.html
   My bibliography  Save this article

A comparative study of abstractive and extractive summarization techniques to label subgroups on patent dataset

Author

Listed:
  • Cinthia M. Souza

    (Pontifical Catholic University of Minas Gerais)

  • Magali R. G. Meireles

    (Pontifical Catholic University of Minas Gerais)

  • Paulo E. M. Almeida

    (Federal Center for Technological Education of Minas Gerais)

Abstract

Patents are an important source of information for measuring the technological advancement of a specific knowledge domain. To facilitate the search for information in patent datasets, classification systems separate documents into groups according to the area of knowledge, and designate names to define their content. The increase in the number of patented inventions leads to the need to subdivide these groups. Since these groups belong to a restricted knowledge domain, naming the generated subcategories can be extremely laborious. This work aims to compare the performance of abstractive and extractive summarization techniques in the task of generating sentences directly associated with the content of patents. The abstractive summarization model was composed by a Seq2Seq architecture and a LSTM network. The training was conducted with a dataset of patent titles and abstracts. The validation process was performed using the ROUGE set of metrics. The results obtained by the generated model were compared with the sentence resulting from an extractive summarization algorithm applied to the task of naming patent groups. The main idea was to help the specialist to name new patent groups created by the clustering systems. The naming experiments were performed on the dataset of abstracts of patent documents. Comparative experiments were conducted using four subgroups of the United States Patent and Trademark Office, which uses the Cooperative Patent Classification system.

Suggested Citation

  • Cinthia M. Souza & Magali R. G. Meireles & Paulo E. M. Almeida, 2021. "A comparative study of abstractive and extractive summarization techniques to label subgroups on patent dataset," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 135-156, January.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:1:d:10.1007_s11192-020-03732-x
    DOI: 10.1007/s11192-020-03732-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-020-03732-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-020-03732-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Xuefeng Wang & Huichao Ren & Yun Chen & Yuqin Liu & Yali Qiao & Ying Huang, 2019. "Measuring patent similarity with SAO semantic analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 1-23, October.
    2. Kim, Jeeeun & Lee, Sungjoo, 2015. "Patent databases for innovation studies: A comparative analysis of USPTO, EPO, JPO and KIPO," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 332-345.
    3. Camus, Caterina & Brancaleon, Riccardo, 2003. "Intellectual assets management: from patents to knowledge," World Patent Information, Elsevier, vol. 25(2), pages 155-159, June.
    4. Juan Carlos Gomez, 2019. "Analysis of the effect of data properties in automated patent classification," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1239-1268, December.
    5. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zheng Liu & Jialing Zhang & Tingting Qin & Yanwen Qu & Yun Li, 2022. "One-to-many comparative summarization for patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1969-1993, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Curci, Ylenia & Mongeau Ospina, Christian A., 2016. "Investigating biofuels through network analysis," Energy Policy, Elsevier, vol. 97(C), pages 60-72.
    2. Mario Corona & Youngjung Geum & Sungjoo Lee, 2017. "Patterns of Protecting Both Technological and Nontechnological Innovation for Service Offerings: Case of the Video-Game Industry," Service Science, INFORMS, vol. 9(3), pages 192-204, September.
    3. Irina Wedel & Michael Palk & Stefan Voß, 2022. "A Bilingual Comparison of Sentiment and Topics for a Product Event on Twitter," Information Systems Frontiers, Springer, vol. 24(5), pages 1635-1646, October.
    4. Mohammed Salem Binwahlan, 2023. "Polynomial Networks Model for Arabic Text Summarization," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 10(2), pages 74-84, February.
    5. Chao Wei & Senlin Luo & Xincheng Ma & Hao Ren & Ji Zhang & Limin Pan, 2016. "Locally Embedding Autoencoders: A Semi-Supervised Manifold Learning Approach of Document Representation," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-20, January.
    6. Su, Hsin-Ning, 2017. "Collaborative and Legal Dynamics of International R&D- Evolving Patterns in East Asia," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 217-227.
    7. Kyuwoong Kim & Kyeongmin Park & Sungjoo Lee, 2019. "Investigating technology opportunities: the use of SAOx analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 45-70, January.
    8. Maksym Polyakov & Morteza Chalak & Md. Sayed Iftekhar & Ram Pandit & Sorada Tapsuwan & Fan Zhang & Chunbo Ma, 2018. "Authorship, Collaboration, Topics, and Research Gaps in Environmental and Resource Economics 1991–2015," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 71(1), pages 217-239, September.
    9. Ding, Ying, 2011. "Community detection: Topological vs. topical," Journal of Informetrics, Elsevier, vol. 5(4), pages 498-514.
    10. Klaus Gugler & Florian Szücs & Ulrich Wohak, 2023. "Start-up Acquisitions, Venture Capital and Innovation: A Comparative Study of Google, Apple, Facebook, Amazon and Microsoft," Department of Economics Working Papers wuwp340, Vienna University of Economics and Business, Department of Economics.
    11. Juan Shi & Kin Keung Lai & Ping Hu & Gang Chen, 2018. "Factors dominating individual information disseminating behavior on social networking sites," Information Technology and Management, Springer, vol. 19(2), pages 121-139, June.
    12. Ganesh Dash & Chetan Sharma & Shamneesh Sharma, 2023. "Sustainable Marketing and the Role of Social Media: An Experimental Study Using Natural Language Processing (NLP)," Sustainability, MDPI, vol. 15(6), pages 1-16, March.
    13. Jun Hong Park & Hyunseog Chung & Ki Hong Kim & Jin Ju Kim & Chulung Lee, 2021. "The Impact of Technological Capability on Financial Performance in the Semiconductor Industry," Sustainability, MDPI, vol. 13(2), pages 1-20, January.
    14. Paola Cerchiello & Giancarlo Nicola, 2018. "Assessing News Contagion in Finance," Econometrics, MDPI, vol. 6(1), pages 1-19, February.
    15. Jiang, Cuiqing & Zhou, Yiru & Chen, Bo, 2023. "Mining semantic features in patent text for financial distress prediction," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    16. Shr-Wei Kao & Pin Luarn, 2020. "Topic Modeling Analysis of Social Enterprises: Twitter Evidence," Sustainability, MDPI, vol. 12(8), pages 1-20, April.
    17. Gissler, Stefan & Oldfather, Jeremy & Ruffino, Doriana, 2016. "Lending on hold: Regulatory uncertainty and bank lending standards," Journal of Monetary Economics, Elsevier, vol. 81(C), pages 89-101.
    18. Wittek, Peter, 2013. "Two-way incremental seriation in the temporal domain with three-dimensional visualization: Making sense of evolving high-dimensional datasets," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 193-201.
    19. Alina Evstigneeva & Mark Sidorovskiy, 2021. "Assessment of Clarity of Bank of Russia Monetary Policy Communication by Neural Network Approach," Russian Journal of Money and Finance, Bank of Russia, vol. 80(3), pages 3-33, September.
    20. Arno de Caigny & Kristof Coussement & Koen W. de Bock & Stefan Lessmann, 2019. "Incorporating textual information in customer churn prediction models based on a convolutional neural network," Post-Print hal-02275958, HAL.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:126:y:2021:i:1:d:10.1007_s11192-020-03732-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.