References
- Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215-239. https://doi.org/10.1016/0378-8733(78)90021-7
- Luhn, H. P. (1960). Key word-in-context index for technical literature (kwic index). American Documentation, 11(4), 288-295. https://doi.org/10.1002/asi.5090110403
- Allan, J., Carbonell, J. G., Doddington, G., Yamron, J., & Yang, Y. (2003). Topic detection and tracking pilot study final report. Retrieved from https://kilthub.cmu.edu/articles/Topic_Detection_and_Tracking_Pilot_Study_Final_Report/6610943
- Antiqueira, L., Oliveira Jr, O. N., da Fontoura Costa, L., & Nunes, M. D. G. V. (2009). A complex network approach to text summarization. Information Sciences, 179(5), 584-599. https://doi.org/10.1016/j.ins.2008.10.032
- Bun, K. K., & Ishizuka, M. (2002, December). Topic extraction from news archive using TF* PDF algorithm. In Proceedings of the Third International Conference on Web Information Systems Engineering, 2002. WISE 2002. (pp. 73-82). IEEE.
- Carbonell, J. G., Yang, Y., Lafferty, J., Brown, R. D., Pierce, T., & Liu, X. (1999). CMU Approach to TDT-2: Segmentation, Detection, and Tracking. Retrieved from https://kilthub.cmu.edu/articles/CMU_Approach_to_TDT-2_Segmentation_Detection_and_Tracking/6621371/files/12117779.pdf
- Daniel, N., Radev, D., & Allison, T. (2003, May). Sub-event based multi-document summarization. In Proceedings of the HLT-NAACL 03 on Text summarization workshop-Volume 5 (pp. 9-16). Association for Computational Linguistics.
- Erkan, G., & Radev, D. R. (2004). Lexrank: Graph-based lexical centrality as salience in text summarization. Journal of artificial intelligence research, 22, 457-479. https://doi.org/10.1613/jair.1523
- Hu, M., & Liu, B. (2004, August). Mining and summarizing customer reviews. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 168-177). ACM.
- Lin, C. Y. (2004). Rouge: A package for automatic evaluation of summaries. In Text summarization branches out (pp. 74-81).
- Lovins, J. B. (1968). Development of a stemming algorithm. Mech. Translat. & Comp. Linguistics, 11(1-2), 22-31.
- Marujo, L., Ling W., Ribeiro, R., Gershman, A., Carbonell, J., Matos, D. M., & Neto, H. P. (2016). Exploring events and distributed representations of text in multi-document summarization. Knowledge-Based Systems, 94, 33-42. https://doi.org/10.1016/j.knosys.2015.11.005
- Mihalcea, R., & Tarau, P. (2004). Textrank: Bringing order into text. In Proceedings of the 2004 conference on empirical methods in natural language processing (pp. 404-411).
- Ouyang, Y., Li, W., Li, S., & Lu, Q. (2011). Applying regression models to query-focused multidocument summarization. Information Processing & Management, 47(2), 227-237. https://doi.org/10.1016/j.ipm.2010.03.005
- Popescu, A. M., & Etzioni, O. (2007). Extracting product features and opinions from reviews. In Natural language processing and text mining (pp. 9-28). Springer, London.
- Walker, C., Strassel, S., Medero, J., & Maeda, K. (2006). ACE 2005 Multilingual Training Corpus. In Linguistic Data Consortium, Philadelphia, 57.
- Wilson, T., Wiebe, J., & Hoffmann, P. (2005). Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing.
- Yang, Y., Carbonell, J. G., Brown, R. D., Pierce, T., Archibald, B. T., & Liu, X. (1999). Learning approaches for detecting and tracking news events. IEEE Intelligent System, 14(4), 32-43.
- Yang, Y., Pierce, T., & Carbonell, J. (1998). A study of retrospective and on-line event detection. In Proceedings of the 21st Annual International ACMSIGIR Conference on Research and Development in Information Retrieval, 28-36.