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2016 | OriginalPaper | Buchkapitel

Summary Generation for Temporal Extractions

verfasst von : Yafang Wang, Zhaochun Ren, Martin Theobald, Maximilian Dylla, Gerard de Melo

Erschienen in: Database and Expert Systems Applications

Verlag: Springer International Publishing

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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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Titel: Summary Generation for Temporal Extractions
verfasst von: Yafang Wang
Zhaochun Ren
Martin Theobald
Maximilian Dylla
Gerard de Melo
Verlag: Springer International Publishing
Buch: Database and Expert Systems Applications
Print ISBN: 978-3-319-44402-4

Electronic ISBN: 978-3-319-44403-1

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-44403-1_23

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