Paper Info
Title
Key Phrase Extraction of Lightly Filtered Broadcast News.
Abstract
This paper explores the impact of light filtering on automatic key phrase extraction (AKE) applied to Broadcast News (BN). Key phrases are words and expressions that best characterize the content of a document. Key phrases are often used to index the document or as features in further processing. This makes improvements in AKE accuracy particularly important. We hypothesized that filtering out marginally relevant sentences from a document would improve AKE accuracy. Our experiments confirmed this hypothesis. Elimination of as little as 10% of the document sentences lead to a 2% improvement in AKE precision and recall. AKE is built over MAUI toolkit that follows a supervised learning approach. We trained and tested our AKE method on a gold standard made of 8 BN programs containing 110 manually annotated news stories. The experiments were conducted within a Multimedia Monitoring Solution (MMS) system for TV and radio news/programs, running daily, and monitoring 12 TV and 4 radio channels.
Year
DOI
Venue
2013
10.1007/978-3-642-32790-2_35
Lecture Notes in Computer Science
Keywords
DocType
Volume
Keyphrase extraction,Speech summarization,Speech browsing,Broadcast News speech recognition
Journal
7499
ISSN
Citations 
PageRank 
0302-9743
6
0.42
References 
Authors
15
6
Name
Order
Citations
PageRank
Luís Marujo122414.86
Ricardo Ribeiro211822.23
David Martins de Matos315229.19
João Paulo Neto429132.69
A. Gershman531651.85
Jaime G. Carbonell65019724.15