Abstract
The TIPSTER Text Summarization Evaluation (SUMMAC) has established definitively that automatic text summarization is very effective in relevance assessment tasks. Summaries as short as 17% of full text length sped up decision-making by almost a factor of 2 with no statistically significant degradation in F-score accuracy. SUMMAC has also introduced a new intrinsic method for automated evaluation of informative summaries.
Cite
CITATION STYLE
Mani, I., House, D., Klein, G., Hirschman, L., Firmin, T., & Sundheim, B. (1999). The TIPSTER SUMMAC text summarization evaluation. In 9th Conference of the European Chapter of the Association for Computational Linguistics, EACL 1999 (pp. 77–85). Association for Computational Linguistics (ACL). https://doi.org/10.3115/977035.977047
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
