Summary generation for temporal extractions

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationDatabase and Expert Systems Applications - 27th International Conference, DEXA 2016, Proceedings
EditorsSven Hartmann, Hui Ma
PublisherSpringer Verlag
Number of pages17
ISBN (Print)9783319444024
StatePublished - 2016
Externally publishedYes
Event27th International Conference on Database and Expert Systems Applications, DEXA 2016 - Porto, Portugal
Duration: Sep 5 2016Sep 8 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9827 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other27th International Conference on Database and Expert Systems Applications, DEXA 2016

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


  • Knowledge harvesting
  • Summarization
  • Temporal information extraction

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