Extractive Summarization of Broadcast News: Comparing Strategies for European Portuguese

From HLT@INESC-ID

The printable version is no longer supported and may have rendering errors. Please update your browser bookmarks and please use the default browser print function instead.
Ricardo Daniel Ribeiro
Ricardo Daniel Ribeiro

Date

  • 15:00, September 14, 2007
  • 3rd floor meeting room

Speaker

Abstract

This work compares three methods for extractive summarization of Portuguese broadcast news: feature-based, Maximal Marginal Relevance, and Latent Semantic Analysis. The main goal is to understand the level of agreement among the automatic summaries and how they compare to summaries produced by non-professional human summarizers. Results were evaluated using the ROUGE-L metric. Maximal Marginal Relevance performed close to human summarizers. Both feature-based and Latent Semantic Analysis automatic summarizers performed close to each other and worse than Maximal Marginal Relevance, when compared to the summaries done by the human summarizers.