Abstract
This paper focuses on continuous news documents and presents a method for extractive multi-document summarization. Our hypothesis about salient, key sentences in news documents is that they include words related to the target event and topic of a document. Here, an event and a topic are the same as Topic Detection and Tracking (TDT) project: an event is something that occurs at a specific place and time along with all necessary preconditions and unavoidable consequences, and a topic is defined to be “a seminal event or activity along with all directly related events and activities.” The difficulty for finding topics is that they have various word distributions. In addition to the TF-IDF term weighting method to extract event words, we identified topics by using two models, i.e., Moving Average Convergence Divergence (MACD) for words with high frequencies, and Latent Dirichlet Allocation (LDA) for low frequency words. The method was tested on two datasets, NTCIR-3 Japanese news documents and DUC data, and the results showed the effectiveness of the method.
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Fukumoto, F., Suzuki, Y., Takasu, A., Matsuyoshi, S. (2016). Identification of Event and Topic for Multi-document Summarization. In: Vetulani, Z., Uszkoreit, H., Kubis, M. (eds) Human Language Technology. Challenges for Computer Science and Linguistics. LTC 2013. Lecture Notes in Computer Science(), vol 9561. Springer, Cham. https://doi.org/10.1007/978-3-319-43808-5_23
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