In this study, we analyzed the effects of applying different levels of stemming approaches such as fixed-length word truncation and morphological analysis for multi-document summarization (MDS) on Turkish, which is an agglutinative 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 truncation approach performs slightly better than no stemming, whereas applying complex morphological analysis does not improve Turkish MDS.
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