This paper describes an empirical study of the “Information Synthesis” task, defined as the process of (given a complex information need) extracting, organizing and inter-relating the pieces of information contained in a set of relevant documents, in order to obtain a comprehensive, non redundant report that satisfies the information need. Two main results are presented: a) the creation of an Information Synthesis testbed with 72 reports manually generated by nine subjects for eight complex topics with 100 relevant documents each; and b) an empirical comparison of similarity metrics between reports, under the hypothesis that the best metric is the one that best distinguishes between manual and automatically generated reports. A metric based on key concepts overlap gives better results than metrics based on n-gram overlap (such as ROUGE) or sentence overlap.
CITATION STYLE
Amigó, E., Gonzalo, J., Peinado, V., Peñas, A., & Verdejo, F. (2004). An empirical study of information synthesis tasks. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (pp. 207–214). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1218955.1218982
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