References
- Berry, M. W., Dumais, S. T., & O’Brien, G. W. (1995). Using linear algebra for intelligent information retrieval. Siam Review, 37(4), 573–595.
- Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
- Carbonell, Jaime, & Goldstein. (1998). The use of MMR, diversity-based reranking for reordering documents and producing summaries. Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, 335–336.
- Cohan, A., & Goharian, N. (2015). Scientific article summarization using citation-context and article’s discourse structure. Proceedings of Conference on Empirical Methods in Natural Language Processing, 390–400.
- Divoli, A., Nakov, P., & Hearst, M. A. (2012). Do peers see more in a paper than its authors? Advances in Bioinformatics, 2012(2012), 750214.
- Elkiss, A., Shen, S., Fader, A., States, D., & Radev, D. (2008). Blind men and elephants: What do citation summaries tell us about a research article? Journal of the American Society for Information Science and Technology, 59(1), 51–62.
- Fahad, A., Alshatri, N., Tari, Z., Alamri, A., Khalil, I., Zomaya, A. Y., Foufou, S., & Bouras, A. (2014). A survey of clustering algorithms for big data: taxonomy and empirical analysis. Emerging Topics in Computing IEEE Transactions on, 2(3), 267–279.
- Fellbaum, C., & Miller, G. (1998). WordNet: An electronic lexical database. Cambridge, MA: MIT Press.
- Jaidka, K., Khoo, C., & Na, J. C. (2013). Deconstructing human literature reviews - A framework for multi-document summarization. The Workshop on European Natural Language Generation, 127, 125–135.
- Lee, D. D. (2000). Algor ithms for nonne gative matrix factorization. Advances in Neural Information Processing Systems, 13(6), 556–562.
- Liu, X. (2013). Generating metadata for cyberlearning resources through information retrieval and meta-search. Journal of the American Society for Information Science and Technology, 64(4): 771–786.
- Maricic, S., Spaventi, J., Pavicic, L., & Pifat-Mrzljak, G. (1998). Citation context versus the frequency counts of citation histories. Journal of the Association for Information Science & Technology, 49(6), 530–540.
- Marujo, L., Ribeiro, R., Matos, D. M. D., Joao P. Neto, Gershman, A., & Carbonell, J. (2015). Extending a single-document summarizer to multi-document: a hierarchical approach. Computer Science, 176–181.
- Mikolov, T., Le, Q. V., & Sutskever, I. (2013). Exploiting Similarities among Languages for Machine Translation. Computer Science, 1–10.
- Nenkova, A., & McKeown, K. (2001). Automatic summarization. Association for Computational Linguistic, 39th Annual Meeting and 10th Conference of the European Chapter, Proceedings of the Student Research Workshop and Tutorial Abstracts, 5(3), 1–42.
- Osiński, S., & Weiss, D. (2005a). Carrot2: Design of a flexible and efficient web information retrieval framework. Proceedings of the Third International Atlantic Web Intelligence Conference, 439–444.
- Osiński, S., & Weiss, D. (2005b). A concept-driven algorithm for clustering search results. IEEE Intelligent Systems, 20(3), 48–54.
- Qazvinian, V., & Radev, D. R. (2008). Scientific paper summarization using citation summary networks. Proceedings of International Conference on Computational Linguistics, 689–696.
- Rada, R., Mili, H., Bicknell, E., & Blettner, M. (1989). Development and application of a metric on semantic nets. IEEE Transactions on Systems Man & Cybernetics, 19(1), 17–30.
- Salton, G., & Yu, C. T. (1973). On the construction of effective vocabularies for information retrieval. Acm Sigplan Notices, 9(3), 48–60.
- Sarkar, K., Saraf, K., & Ghosh, A. (2015). Improving graph based multidocument text summarization using an enhanced sentence similarity measure. Proceedings of IEEE nternational Conference on Recent Trends in Information Systems, 359–365.
- Stefanowski, J., & Weiss, D. (2003). Carrot2 and language properties in web search results clustering. Proceedings of the First International Atlantic Web Intelligence Conference, 2663, 240–249.
- Tandon, N., & Jain, A. (2012). Citation context sentiment analysis for structured summarization of research papers. Proceedings of 35th German Conference on Artificial Intelligence, 1–5.
- Valizadeh, M., & Brazdil, P. (2015). Density-based graph model summarization: attaining better performance and efficiency. Intelligent Data Analysis, 19(3), 617–629.
- Yang, L., Cai, X., Pan, S., Dai, H., & Mu, D. (2017). Multi-document summarization based on sentence cluster using non-negative matrix factorization. Journal of Intelligent & Fuzzy Systems, 33(1), 1–13.
- Yang, S., Lu, W., Yang, D., Li, X., Wu, C., & Wei, B. (2016). KeyphraseDS: Automatic generation of survey by exploiting keyphrase information. Neurocomputing, 224, 58–70.
- Yang, Y., & Pedersen, J. O. (1997). A Comparative Study on Feature Selection in Text Categorization. Proceedings of the 14th International Conference on Machine Learning, 4(3), 412–420.
- Zhang, R., Li, W., Gao, D., & Ouyang, Y. (2013). Automatic twitter topic summarization with speech acts. IEEE Transactions on Audio Speech & Language Processing, 21(3), 649–658.
- Zechner, K. (1996). Fast generation of abstracts from general domain text corpora by extracting relevant sentences. Proceedings of the 16th Conference on Computational linguistics, 2, 986–989.