Please use this identifier to cite or link to this item:
|Title:||Event-based summarization using a centrality-as-relevance model|
de Matos, D. M.
Neto, J. P.
Automatic key phrase extraction
|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.|
|Appears in Collections:||CTI-RI - Artigos em revistas científicas internacionais com arbitragem científica|
Files in This Item:
|Event-based summarization using a centrality-as-relevance model.pdf||Versão Editora||1.05 MB||Adobe PDF||View/Open Request a copy|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.