Key Phrase Extraction of Lightly Filtered Broadcast News
This work addresses the need for more accurate key phrase extraction in multimedia monitoring systems, though it is incremental as it builds on existing supervised learning methods.
The paper tackled the problem of improving automatic key phrase extraction (AKE) accuracy for Broadcast News by testing the hypothesis that light filtering of marginally relevant sentences enhances performance, resulting in a 2% improvement in precision and recall when eliminating 10% of sentences.
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.