Event graphs for information retrieval and multi-document summarization

With the number of documents describing real-world events and event-oriented information needs rapidly growing on a daily basis, the need for efficient retrieval and concise presentation of event-related information is becoming apparent. Nonetheless, the majority of information retrieval and text su...

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Bibliographic Details
Published in:Expert systems with applications 2014-11, Vol.41 (15), p.6904-6916
Main Authors: GLAVAS, Goran, SNAJDER, Jan
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Event extraction
Exact sciences and technology
Information extraction
Information retrieval
Information retrieval. Graph
Information systems. Data bases
Memory organisation. Data processing
Multi-document summarization
Natural language processing
Software
Speech and sound recognition and synthesis. Linguistics
Theoretical computing
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Summary:With the number of documents describing real-world events and event-oriented information needs rapidly growing on a daily basis, the need for efficient retrieval and concise presentation of event-related information is becoming apparent. Nonetheless, the majority of information retrieval and text summarization methods rely on shallow document representations that do not account for the semantics of events. In this article, we present event graphs, a novel event-based document representation model that filters and structures the information about events described in text. To construct the event graphs, we combine machine learning and rule-based models to extract sentence-level event mentions and determine the temporal relations between them. Building on event graphs, we present novel models for information retrieval and multi-document summarization. The information retrieval model measures the similarity between queries and documents by computing graph kernels over event graphs. The extractive multi-document summarization model selects sentences based on the relevance of the individual event mentions and the temporal structure of events. Experimental evaluation shows that our retrieval model significantly outperforms well-established retrieval models on event-oriented test collections, while the summarization model outperforms competitive models from shared multi-document summarization tasks.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2014.04.004