Abstract
Intellectual property document mainly patents provide exclusive rights to the invention. These technical documents are much valuable to gain insights about the latest trends in the technology and for competitive advancements, R & D management and for future innovations. Since the patent documents are lengthy and contain legal information, it is difficult to process multiple documents manually. This paper proposes Semantic Query-based Summarization System (SQPSS) where the patent search query provided by the patent analyst is enriched with the domain knowledge base, and the retrieved related documents are summarized in an extractive way with the help of Restricted Boltzmann Machine (RBM). Experiments are carried out with search queries from smartphone domain, and the summarization results are evaluated regarding precision, recall and compression rate. The results show an improvement over the existing Open Text Summarizer tool and the summary produced has an average compression rate of around 30%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Al-Shboul, B., Myaeng, S.H.: Wikipedia-based query phrase expansion in patent class search. Inf. Retrieval 17(5–6), 430–451 (2014)
Alberts, D., et al.: Introduction to patent searching. In: Lupu, M., Mayer, K., Kando, N., Trippe, A. (eds.) Current Challenges in Patent Information Retrieval. TIRS, vol. 37, pp. 3–45. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-662-53817-3_1
Azzam, S., Humphreys, K., Gaizauskas, R.: Using coreference chains for text summarization. In: Proceedings of the Workshop on Conference and its Applications, pp. 77–84. Association for Computational Linguistics (1999)
Berger, A., Mittal, V.O.: Query-relevant summarization using FAQs. In: Proceedings of the 38th Annual Meeting on Association for Computational Linguistics, pp. 294–301. Association for Computational Linguistics (2000)
Cao, Z., Wei, F., Dong, L., Li, S., Zhou, M.: Ranking with recursive neural networks and its application to multi-document summarization. In: AAAI, pp. 2153–2159 (2015)
Chuang, W.T., Yang, J.: Extracting sentence segments for text summarization: a machine learning approach. In: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 152–159. ACM (2000)
Duraiswamy, K.: An approach for text summarization using deep learning algorithm (2014)
Edmundson, H.P.: New methods in automatic extracting. J. ACM (JACM) 16(2), 264–285 (1969)
Gaff, B.M., Rubinger, B.: The significance of prior art. Computer 8, 9–11 (2014)
Ganguly, D., Leveling, J., Magdy, W., Jones, G.J.: Patent query reduction using pseudo relevance feedback. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 1953–1956. ACM (2011)
Lopez, P., Romary, L.: Experiments with citation mining and key-term extraction for prior art search. In: CLEF 2010-Conference on Multilingual and Multimodal Information Access Evaluation (2010)
Luhn, H.P.: The automatic creation of literature abstracts. IBM J. Res. Dev. 2(2), 159–165 (1958)
Magdy, W., Jones, G.J.: Applying the kiss principle for the CLEF-IP 2010 prior art candidate patent search task (2010)
Mahdabi, P., Crestani, F.: Patent query formulation by synthesizing multiple sources of relevance evidence. ACM Trans. Inf. Syst. (TOIS) 32(4), 16 (2014)
Mani, I.: Automatic Summarization, p. 3. John Benjamins Publishing, Amsterdam (2001)
Marcu, D.: The rhetorical parsing of natural language texts. In: Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics, pp. 96–103. Association for Computational Linguistics (1997)
Radev, D.R., Jing, H., Budzikowska, M.: Centroid-based summarization of multiple documents: sentence extraction, utility-based evaluation, and user studies. In: Proceedings of the 2000 NAACL-ANLP Workshop on Automatic summarization, vol. 4. pp. 21–30. Association for Computational Linguistics (2000)
Torres-Moreno, J.M.: Automatic Text Summarization. Wiley, New York (2014)
Toutanova, K., Klein, D., Manning, C.D., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, vol. 1, pp. 173–180. Association for Computational Linguistics (2003)
Trappey, A.J., Trappey, C.V.: An R&D knowledge management method for patent document summarization. Industr. Manage. Data Syst. 108(2), 245–257 (2008)
Zhang, Y., Er, M.J., Zhao, R., Pratama, M.: Multiview convolutional neural networks for multidocument extractive summarization. IEEE Trans. Cybern. 47(10), 3230–3242 (2017)
Acknowledgments
This research is sponsored by Visveshvaraya PhD Scheme for Electronics & IT Proceedings No. 3408/PD6/DeitY/2015.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Girthana, K., Swamynathan, S. (2019). Semantic Query-Based Patent Summarization System (SQPSS). In: Akoglu, L., Ferrara, E., Deivamani, M., Baeza-Yates, R., Yogesh, P. (eds) Advances in Data Science. ICIIT 2018. Communications in Computer and Information Science, vol 941. Springer, Singapore. https://doi.org/10.1007/978-981-13-3582-2_13
Download citation
DOI: https://doi.org/10.1007/978-981-13-3582-2_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-3581-5
Online ISBN: 978-981-13-3582-2
eBook Packages: Computer ScienceComputer Science (R0)