Automatic Generation of Comparative Summary for Scientific Literature

doi:10.23940/ijpe.18.07.p21.15701579

Abstract

Abstract:

In this paper, we propose a comparative summary generation method and conduct key technologies research. We collect prior knowledge from the Internet via a light knowledge structure, extract core information from original literature, divide subtopics of two major topics with AGNES clustering to get the common and independent subtopics, and get comparative information with subtopics alignment and property alignment. We test the performance of each module to prove the validity of the proposed methods. Finally, we design and develop a comparative summary generation system, and the application in the nursing field shows that it can present users with useful information to facilitate the scientific research process.

Submitted on April 1, 2018; Revised on May 11, 2018; Accepted on June 25, 2018
References: 11

Yao Liu, Yuqing Yang, and Yi Huang. Automatic Generation of Comparative Summary for Scientific Literature [J]. Int J Performability Eng, 2018, 14(7): 1570-1579.

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References 0

	M. Campr and K. Jezek, “Comparative Summarization via Latent Semantic Analysis,” In WSEAS International Conference. Proceedings. Recent Advances in Computer Engineering Series, no. 7. WSEAS, 2012.
	M. Campr and K. Jezek, “Comparative Summarization via Latent Dirichlet Allocation,” in CEUR Workshop Proceedings, 2013, vol. 971, pp. 80–86.
	P. Chitra, R. Baskaran, and K. Sarukesi. “Query Sensitive Comparative Summarization of Search Results Using Concept Based Segmentation,” arXiv preprint arXiv:1201.2304, 2012
	H. P. Edmundson, “New Methods in Automatic Extracting,” Journal of the ACM (JACM), vol. 16, no. 2, pp. 264–285, 1969.
	X. Gong and Y. Liu, “Research on Construction of Integrated Semantic Crawler,” ICIC Express Letters, Part B: Applications, vol. 7, no. 7, pp. 1591–1598, 2016.
	Y. Liu, H. Shi, D. Zheng, and Y. Huang, “Study on Semantic Annotation for Professional Literature,” ICIC Express Letters, Part B: Applications, vol. 5, no. 5, pp. 1383–1389, 2014.
	Y. Liu and R. Wang, “Research on Semantic Metadata Online Auxiliary Construction Platform and Key Technologies,” ICIC Express Letters, Part B: Applications, vol. 4, no. 4, pp. 897–904, 2013.
	Y. Liu, D. Zheng, and Z. Guo, “Research on Feature Acquisition and Key Expression Technology of Knowledge-Intensive Text,” ICIC Express Letters, Part B: Applications, vol. 5, no. 1, pp. 57–64, 2014.
	C. Shen and T. Li, “Multi-Document Summarization via The Minimum Dominating Set,” In Proceedings of the 23rd International Conference on Computational Linguistics, vol. 2, pp. 984–992, 2010.
	D. Wang, S. Zhu, T. Li, and Y. Gong, “Comparative Document Summarization via Discriminative Sentence Selection,” ACM Transactions on Knowledge Discovery from Data, vol. 6, no. 3, pp. 1–18, 2012.
	C. Zhai, A. Velivelli, and B. Yu, “A Cross-Collection Mixture Model for Comparative Text Mining,” in Proceedings of the 2004 ACM SIGKDD International Conference On Knowledge Discovery And Data Mining - KDD ’04, 2004, p. 743.