Key Phrase Extraction of Lightly Filtered Broadcast News

TL;DR

This study demonstrates that light filtering of broadcast news documents enhances automatic key phrase extraction accuracy, with minimal sentence removal leading to measurable improvements.

Contribution

It shows that removing marginally relevant sentences improves key phrase extraction accuracy in broadcast news, validated through experiments on a supervised learning approach.

Findings

10% sentence removal improves AKE precision and recall by 2%

Filtering marginally relevant sentences enhances AKE performance

Experiments conducted on 8 broadcast news programs with 110 stories

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.