Abstract
Recent advances in knowledge harvesting have enabled us to collect large amounts of facts about entities from Web sources. A good portion of these facts have a temporal scope that, for example, allows us to concisely capture a person’s biography. However, raw sets of facts are not well suited for presentation to human end users. This paper develops a novel abstraction-based method to summarize a set of facts into natural-language sentences. Our method distills temporal knowledge from Web documents and generates a concise summary according to a particular user’s interest, such as, for example, a soccer player’s career. Our experiments are conducted on biography-style Wikipedia pages, and the results demonstrate the good performance of our system in comparison to existing text-summarization methods.
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Acknowledgments
We thank the anonymous reviewers for their valuable comments. This project was sponsored by National Natural Science Foundation of China (No. 61503217), Shandong Provincial Natural Science Foundation of China (No. ZR2014FP002), and The Fundamental Research Funds of Shandong University (Nos. 2014TB005, 2014JC001).
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Wang, Y., Ren, Z., Theobald, M., Dylla, M., de Melo, G. (2016). Summary Generation for Temporal Extractions. In: Hartmann, S., Ma, H. (eds) Database and Expert Systems Applications. DEXA 2016. Lecture Notes in Computer Science(), vol 9827. Springer, Cham. https://doi.org/10.1007/978-3-319-44403-1_23
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DOI: https://doi.org/10.1007/978-3-319-44403-1_23
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