Event-based summarization using a centrality-as-relevance model
详细信息 查看全文
- 作者：Luís Marujo ; Ricardo Ribeiro ; Anatole Gershman…
- 关键词：Event detection ; Extractive summarization ; Passage retrieval ; Automatic key phrase extraction ; Centrality
- 刊名：Knowledge and Information Systems
- 出版年：2017
- 出版时间：March 2017
- 年：2017
- 卷：50
- 期：3
- 页码：945-968
- 全文大小：
- 刊物类别：Computer Science
- 刊物主题：Information Systems and Communication Service; IT in Business;
- 出版者：Springer London
- ISSN：0219-3116
- 卷排序：50
文摘
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.