Paper: Analyzing Stemming Approaches for Turkish Multi-Document Summarization

ACL ID D14-1077
Title Analyzing Stemming Approaches for Turkish Multi-Document Summarization
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

In this study, we analyzed the effects of ap- plying different levels of stemming approaches such as fixed-length word truncation and mor- phological analysis for multi-document sum- marization (MDS) on Turkish, which is an ag- glutinative and morphologically rich language. We constructed a manually annotated MDS data set, and to our best knowledge, reported the first results on Turkish MDS. Our results show that a simple fixed-length word trun- cation approach performs slightly better than no stemming, whereas applying complex mor- phological analysis does not improve Turkish MDS.