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Knowledge and Information Systems

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21.06.2016 | Regular Paper

Event-based summarization using a centrality-as-relevance model

verfasst von: Luís Marujo, Ricardo Ribeiro, Anatole Gershman, David Martins de Matos, João P. Neto, Jaime Carbonell

Erschienen in: Knowledge and Information Systems | Ausgabe 3/2017

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Abstract

Event detection is a fundamental information extraction task, which has been explored largely in the context of question answering, topic detection and tracking, knowledge base population, news recommendation, and automatic summarization. In this article, we explore an event detection framework to improve a key phrase-guided centrality-based summarization model. Event detection is based on the fuzzy fingerprint method, which is able to detect all types of events in the ACE 2005 Multilingual Corpus. Our base summarization approach is a two-stage method that starts by extracting a collection of key phrases that will be used to help the centrality-as-relevance retrieval model. We explored three different ways to integrate event information, achieving state-of-the-art results in text and speech corpora: (1) filtering of nonevents, (2) event fingerprints as features, and (3) combination of filtering of nonevents and event fingerprints as features.

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Titel: Event-based summarization using a centrality-as-relevance model
verfasst von: Luís Marujo
Ricardo Ribeiro
Anatole Gershman
David Martins de Matos
João P. Neto
Jaime Carbonell
Publikationsdatum: 21.06.2016
Verlag: Springer London
Erschienen in: Knowledge and Information Systems / Ausgabe 3/2017
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI: https://doi.org/10.1007/s10115-016-0966-4

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