The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
New Methods in Automatic Extracting
Journal of the ACM (JACM)
Generic text summarization using relevance measure and latent semantic analysis
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Using maximum entropy for sentence extraction
AS '02 Proceedings of the ACL-02 Workshop on Automatic Summarization - Volume 4
Mixed-source multi-document speech-to-text summarization
MMIES '08 Proceedings of the Workshop on Multi-source Multilingual Information Extraction and Summarization
Active learning of extractive reference summaries for lecture speech summarization
BUCC '09 Proceedings of the 2nd Workshop on Building and Using Comparable Corpora: from Parallel to Non-parallel Corpora
Active learning with semi-automatic annotation for extractive speech summarization
ACM Transactions on Speech and Language Processing (TSLP)
Revisiting centrality-as-relevance: support sets and similarity as geometric proximity
Journal of Artificial Intelligence Research
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This paper presents the comparison between 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.